I was hoping I could get some insight into the tooling that you use (or would use) for some production RL work? I'm mostly doing it all on my home machine as a fun side project. I've got the following existing infrastructure: - An interface based loosely on the standard RL setup. I'm thinking about adapting it to fit Acme to let it do more heavy lifting since I quite like `Haiku`, `rlax` and the rest of what they do. - I've got some models across languages (Pytorch and Jax) and this has been causing me some headache trying to make sure everything is abstract enough. Should I just stick to one language and make sure all my friends just use that same language? - I'm currently using comet-ml for my experiment tracking, and for the most part I like it. However, I'm now looking around to see what's out there and I'm a little overwhelmed by (1) how many tools there are and (2) how some of them seem to "overlap" so I don't really know how to compose them. - configs all stored in a python file in a separate repo that is kept synced between my other repos. - I currently store my agent experiences (off policy) in a database that I later query to rapidly fill up the replay buffer. The limitation is that this is for a single agent. What drew me to Acme is that it seems to allow multiple agents to all use the same buffer? _____________ tl;dr 1) Has anyone used Acme? I'm thinking of moving my project to it, but it might end up being a lot of effort for very little reward 2) How do you and your teams handle multiple languages? Do you just have abstract gym wrappers that convert data? 3) What tools do you use and how do you compose them together? I'm so so lost trying to navigate this space 4) How do you keep your configs synced when they are used between repos? submitted by /u/whynotmehmm [link] [comments]  ( 1 min )

My dates are as follows: ​ https://preview.redd.it/9htkypafgeq81.png?width=198&format=png&auto=webp&s=35e5475cf71364b8958b34e6100bd0ada2dc756d What I would like is to be able to map the following to a script: - Value 1440/1 in column FROM represented value 144019/1 in column TO. - Find value 144019/1 again in column FROM. - If found, take the value in column TO and find it again in column FROM. Not found, stop searching. ​ Note: value 1440/1 does not have to be the initial value. In my data, 1440/1 can refer to another value again that is from column TO. ​ I would like the following as output: - 1440/1, 144019/1, 144019/2; - 1440/1, 144018/1, 144018/2, 6038/1. submitted by /u/Silver-Panda2518 [link] [comments]  ( 1 min )

Hi, I'm trying to run QAT on a MobileNetV2 based model but having some issues hitting the same training losses in the QAT phase as I did in the not-QAT phase. As a test, I'd trained the network for 1 epoch, then trained the QAT phase for 5 epochs and managed to get the same loss (actually lower). However, after training the model (non-QAT part) for 150 epochs, the QAT phase is really struggling to get down to the same loss. In my first test it dropped then completely levelled off for 2-3 epochs then nosedived again for another epoch, I'm not seeing the same in this longer train though. I was wondering if anyone has any advice on things like, should the learning rate be reset at the start of the QAT phase or should it carry on from where the training left off? I'm using Adam as the optimiser in the first phase, is that still ok in the second phase. Any other things that I could try? I did read a paper on improving quantization loss in MobileNet by L2 weighting the separable conv weights and swapping out ReLU6 for ReLU but I wasn't really seeing the same benefit as the paper (https://arxiv.org/pdf/1803.08607.pdf) did in my tests, I was getting a worse initial network. Thanks for any insight that anyone can provide! submitted by /u/ColdChancer [link] [comments]  ( 1 min )

​ https://reddit.com/link/trno8g/video/84uuyln8ceq81/player Hi everyone, here's an interactive demo I made for paper SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches Demo: http://47.57.135.203:8001/ Paper: https://arxiv.org/abs/2111.15078 Project page: https://zengxianyu.github.io/sketchedit/ Code: https://github.com/zengxianyu/sketchedit submitted by /u/Educational_Ebb2502 [link] [comments]  ( 1 min )

Yesterday's answer: Pandas View Poll submitted by /u/daichrony [link] [comments]

This weekend we (Hugh, Mostafa, and Chris from Meta AI) will be running a tutorial on Autotuning and Reinforcement Learning for compilers using CompilerGym at CGO’22. Join us for a hands-on session that takes you from “zero to RL” in three hours! The tutorial stats 1:30pm ET on Saturday April 2nd. Full schedule: https://conf.researchr.org/program/cgo-2022/program-cgo-2022/?date=Sat%202%20Apr%202022 submitted by /u/melhoushi [link] [comments]

Hey all, I'm going through research to figure out how the Date Inference protocols might be implemented. Given a text containing the phrase "5 days from now", I would need it to infer the date April 3rd. The 'now' part is a trivial problem, but the inference is something I'm struggling with. I could use regex but all the possible edge cases are tricky. The inference would need to work on unstructured cases like "April 23rd", "Next Sunday", etc. It would need to work forwards and backward (5 days ago etc.) ​ Any great papers/resources? I searched for date inference, but nothing similar to what I'm looking for submitted by /u/ISeeThings404 [link] [comments]  ( 1 min )

Paper: https://arxiv.org/abs/2203.13448 Code: https://github.com/lijuncheng16/AudioTaggingDoneRight For anyone who's interested in AudioSet (2million youtube videos' sound). This is the SOTA comparison of models and training procedures. submitted by /u/billyli_16 [link] [comments]

The objective is simple, a kit with some small hardware is given to us (nuts, bolts, washers, etc). Using our laptop cameras, we need to develop a model that is able to accurate classify what object is what when placed infront of the camera. There can be any number of objects in any orientation, displayed on any color surface. What is the best way to approach this problem, what is a good model structure (high level) and what can I do to be a step above the competition. submitted by /u/Certified_User [link] [comments]  ( 1 min )

I'm a researcher who is deeply interested in deep generative models. There are excellent textbooks I want to read if time allows, such as: Probabilistic ML: https://probml.github.io/pml-book/book1.html PRML: https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf However, there are also many papers I have to read, new theories I have to learn, and works I need to finish. The problem is that reading textbooks could deepen the fundamental understanding of the field but rarely gives an immediate reward. Practically, reading a textbook from start to end can take > 1000 hours, in which one can read more than a hundred papers. Given the situation, I have studied basic stuff only when I need them for my research (you know, publish or perish). ​ However, I think the time to read textbooks will decrease rather than increase, and only junior researchers will be able to afford to read them. It means that if I don't read them now, I won't be able to read later. Is there any general advice on this? submitted by /u/SnooPandas3529 [link] [comments]  ( 6 min )

I have a project featuring 5+ years of data detailing mechanic reports. Essentially, I want to use ML to build a model that can make suggested actions to fix an issue based on these mechanic reports. For example, if a user typed in that a car “makes a squeaky sound” then it suggests three courses of action that may fix the issue based on similar issues and solutions detailed in the mechanic reports. Furthermore, when returning these suggestions, I want the user to see some sort of score indicating how likely it is to fix the issue (i.e. option A worked 97% of the time, option B 2% of the time, and option C 1% of the time). I also want the user to be able to try the options and give feedback on if they fixed the issue. My brain immediately went to a recommender system, but I don’t have much experience with creating them. Can they do all of the above (recommend solutions, score solutions, and allow for user input to keep training the model) or do I need to somehow pair with another method/model? I’m just not sure where to start. submitted by /u/ambiguousalmond [link] [comments]  ( 1 min )

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Avoid over fitting in iterative pruning For iterative pruning algorithms referring to research papers like: ​ Learning both Weights and Connections for Efficient Neural Networks Deep Compression Comparing Rewinding and Fine-Tuning in Neural Network Pruning I have found that during these pruning rounds, the pruned sub-network starts to overfit excessively, with training accuracy approaching almost 100%. This can be attributed to the fact that the surviving trained parameters are not reinitialized to either the randomly initialized values or to a previous value from earlier in the training. Whereas, for "The Lottery Ticket Hypothesis" and it's family of related research papers such as: The Lottery Ticket Hypothesis Stabilizing the Lottery Ticket Hypothesis One ticket to win them all Deconstructing Lottery Tickets such overfitting is usually not observed due to the weight rewinding scheme. Since, the original & unpruned deep learning architecture is already trained with strategies such as: data augmentation, weight decay, learning rate schedule, etc., the resulting iterative pruning rounds result in overfitting. Can you suggest a way to avoid these overfitting during these iterative pruning rounds? submitted by /u/grid_world [link] [comments]  ( 2 min )

Hi, I'm a student at the University of Glasgow building a linux app that is trying to use CNNs to teach people the ASL (american sign language) alphabet. We just released the first version of our software which (although admittedly buggy) is worth sharing with interested communities. In brief, a MobileNetv2 model is trained on kaggle data for each sign in the ASL alphabet, this is executed within the OpenCV framework and run on camera frames of the user in real time. The user is challenged to make different signs and rewarded when the correct sign is made. We would love for interested people to try out our software and let us know about enhancement ideas or any bugs they may find. If you are interested in the project, please head over to our GitHub to have a look: https://github.com/albanjoseph/Signapse You can also follow us on Facebook: https://www.facebook.com/Signapse-125793226671815 Twitter: https://twitter.com/GU_Signapse and YouTube: https://www.youtube.com/channel/UCh2uG2pYoSloEU0IFeqDQMA Cheers! Signapse Team submitted by /u/rossythebossy [link] [comments]  ( 1 min )

You can now build a space comparing Hugging Face Models and Spaces or create clones of them with Model Comparator Space Builder 📷📷📷 https://huggingface.co/spaces/farukozderim/Model-Comparator-Space-Builder ​ https://preview.redd.it/t40n9amr0cq81.png?width=1813&format=png&auto=webp&s=f151ea9060f7c5b43f8dbcf3f91d1c308bdbb422 ​ https://preview.redd.it/fx1ztghs0cq81.png?width=1848&format=png&auto=webp&s=f1cfcb2b47831b10744b4d0e114fd21ed725b195 Gradio: https://github.com/gradio-app/gradio Hugging Face: https://huggingface.co/ submitted by /u/Mundane-Apartment224 [link] [comments]

Hi everyone. Our DS & DA team open sourced a Python library that helps in dealing with categorical variables for machine learning algorithms. It leverages Tensorflow/Keras embedding layers and builds a neural network that learns a dense representation of each unique class. This is all packaged inside a regular scikit-learn transformer that can be used within pipelines and can have its hyperparameters optimized with regular sklearn methods. Just do pip install embedding-encoder[tf]. Check out the readme at Github or the blog post for examples. Github: https://github.com/cpa-analytics/embedding-encoder PyPI: https://pypi.org/project/embedding-encoder/ Blog post: https://cpa-analytics.github.io/embedding-encoder-intro/ This was inspired by the 3rd place solution in the Rossmann Store Sales Kaggle competition. Some implementations have surfaced over the years but we are not aware of working one that integrates well with existing libraries. This is just another preprocessing technique. It can be optimal for your task or not. As always, try multiple approaches and evaluate the results! submitted by /u/rafa10pj [link] [comments]  ( 2 min )

I am building a ML model to classify malware. My input data are windows binaries which get de-compiled into functions. From these functions I create embeddings, each is 150 float numbers. https://preview.redd.it/4ejwz7ukubq81.png?width=581&format=png&auto=webp&s=cbde0b534a8424f2a17ea5f1c77fccd2860685f5 Problem: Each binary has a variable amount of functions. Some may have 40 functions while others may have over 1000. Most will have between 50 and 200. The order of the functions is not important. Question: What is the best way to deal with these variable amount of input. Hashing trick? Or Deep sets? What would you recommend? submitted by /u/laddi27 [link] [comments]  ( 1 min )

Jonathan came on the Weaviate Podcast to discuss the story of MosaicML, their new open-source Python library for Efficient Deep Learning called Composer, Pareto Curves of Training Time X Accuracy, Model Surgey augmentations, Maximizing CPU and GPU throughput, and many more! I hope you find this useful, happy to continue discussions of what Jonathan presented! https://www.youtube.com/watch?v=ZiBkspwrICA submitted by /u/HenryAILabs [link] [comments]  ( 1 min )

Hey everyone! I'd like to use a large language model like T0pp or GPT-NeoX-20B to take a natural-language input from a user and map it to one of ~2000 possible VS Code command palette commands. Essentially, this is a classification problem of the form "NLP input -> command". The idea is to let users give voice input in natural language and then have the model figure out what command they most likely want to activate. Given the number of possible commands I clearly can't rely on prompt design to solve this. It might be a good fit for a model with explicit retrieval augmentation like a memorizing transformer. But that's still a very active area of research without high-quality pre-trained models. Given that, I'm thinking that doing some kind of fine tuning to an existing model is the best bet. But it's unclear to me what the training data should look like... should I just generate a few examples of each command of the form input: "vscode command: 'open new file'", output: "explorer.newFile", and then fine-tune on those? Is there some way to ensure that the model understands that I *always* want it to return one of the commands provided in fine-tuning, instead of arbitrary text? Interested in others' experiences with similar tasks! Background: I'm working on an open source VS Code extension called Clippy AI. Currently it only performs code modifications to the current file and is a thin wrapper around the OpenAI API. But I'd like to use it to automate other editor actions as well! submitted by /u/corbt [link] [comments]  ( 2 min )

As part of our AI Challenge with a health-tech startup: https://omdena.com/blog/pathology-streamlit/ https://preview.redd.it/zew2baykgbq81.png?width=640&format=png&auto=webp&s=e66180724c08f22db3d322d8b1fd6f56e8765a3c submitted by /u/Lordobba [link] [comments]

So basically, I started my internship in business intelligence, and when my boss know that I have a background in machine learning and deep learning. so, he asked me to build a model that predicts a specific number for the next month. so, it time series problem, and the datasets that I have it is very small it starts from May 2019 so it is just 31 rows. And when I plotted the data, it had no clear trend. This pitcher for the graph looks like my dataset here! dataset (sorry I cannot share the dataset because of privacy). So, I started to take the difference in the data to remove the seasonality and log transformation and after that, I bullied the model using the Arima algorithm and LSTM, and prophet. And I applied a prediction interval for the predicted number to get periods and expect the number will be inside this interval. But unfortunately, the actual number (for this month) was out of the interval. So, I decided to look back in a database and I found a feature I think that may help and have a high correlation with the main feature now becoming a multivariate time series problem. so, I tried to use the VAR algorithm but unfortunately, the model also filed and the actual number for each feature was out of interval. This first time for me to build a time series model in the industry for a real dataset and I worked alone. So, there is an approach that can help me to build a better model that I do not follow in my step. Or I should go to my boss tell him cannot build a model for this dataset, especially the data is impacted by a coronavirus. submitted by /u/xxsalehxx140 [link] [comments]  ( 2 min )

Hello everyone, I am a german student and currently writing my masters thesis. It is a rather simple ML task but for my thesis I need to describe the methodology and which process models are used and I am new to this. I found CRISP-DM and its successor ASUM-DM. However, I know that sounds stupid, I am not able to find information on these or useful pdfs. Like the general information and descriptions are accessable but I need an officisl source that I can cite. IBM itself has a link: ftp: //ftp.software.ibm.com/software/data/ sw-library/services/ASUM.pdf However, its not working for me as I have no ftp access. So my question is, does anyone have a link where I can find relevant official information to these models and furthermore are there any other standards or process models to describe the approach of working with data that are rather used in the industry. submitted by /u/terektus [link] [comments]  ( 1 min )

Hello, I am looking at having a neural network take inputs of 8 objects that all have different features/attributes and then an input of the different features/attributes of the environment the objects are in. The output of this would be the rank of each object. The attached image demonstrates a diagram of the neural network. The objects actively interact/compete with each other. I thought about inputting a single object's features and the environment into a neural network with the output as a performance score of the object. However, the object does worse or better depending on what other objects it is competing against. The objects also get better or worse over time, so it may be good to backpropagate and analyze the objects also as a time series. Is it possible to input the object/object features as a matrix? I have not figured out a way to group this data. I was thinking maybe a convolution neural network may work. I am somewhat new to the machine learning world. Any recommendation or help would be great. Thank you https://preview.redd.it/ng7q9haxw7q81.jpg?width=6450&format=pjpg&auto=webp&s=9393006f00583a1a4a9af02044e726559176c403 submitted by /u/hypercar_junkie [link] [comments]  ( 1 min )

Hi, I am intending to write a paper (almost 25-30 pages) about time series clustering. I have done my online research, however, I ll be grateful if you can mention some other resources that might be of interest, either theoretical or applied. It can be blogs about machine learning you find interesting in this area, video series, lectures, lecture notes, whatever. Thank you very much. submitted by /u/jiii95 [link] [comments]  ( 1 min )

Flower is an end-to-end federated learning framework that allows for a smoother transition from simulation-based experimental research to system research on many real-world edge devices. Flower has individual strengths in both domains (i.e., simulation and real-world devices) and the capacity to switch back and forth between the two extremes as needed throughout exploration and development. Researchers present use cases that drive our viewpoint, design goals, the resultant framework architecture, and comparisons to other frameworks in this part. Federated Learning (FL) has shown to be a viable option for enabling edge devices to develop a shared prediction model cooperatively while maintaining their training data on the device, divorcing the capacity to execute machine learning from the requirement to store data in the cloud. However, FL is challenging to implement practically in size and system heterogeneity. Although there are several research frameworks for simulating FL algorithms, none of them facilitate the investigation of scalable FL workloads on heterogeneous edge devices. Flower 0.18 released Thanks to a longer gap than usual, the latest Flower release has more upgrades than any previous release. Also, thanks to the wonderful community for your continuing support and generosity. Continue Reading Paper: https://arxiv.org/pdf/2007.14390.pdf Github: https://github.com/adap/flower https://preview.redd.it/ywtttqlnceq81.png?width=1920&format=png&auto=webp&s=893fbe79c190aa66e293b296e35d4096eb178f97 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Since we are about to create a silicon-based lifeform I was looking up the differences between the carbon and the silicon molecule. https://www.differencebetween.com/difference-between-silicon-and-vs-carbon/#:~:text=The%20key%20difference%20between%20silicon,in%20the%20outer%20energy%20level. submitted by /u/asenz [link] [comments]

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In their recent post on how Google keeps Maps information reliable, the company elaborates how they use machine learning and human operators to block nearly 100 million attempted fraudulent edits to Google Business Profiles. Machine learning, in simple terms, is a sort of artificial intelligence (AI) that lets software applications improve their accuracy at predicting events without having to be explicitly programmed to do so. Machine learning algorithms use past data as input to forecast new output values. The world changed with the introduction of vaccinations, revisions to mask regulations, and new COVID variations in 2021. Accordingly, their Maps community updated Google Maps with further information about their nearby areas. Their contributions have helped Google provide updated company information, such as a location’s hours of operation or its health and safety regulations, for 30% more firms in 2021 than 2020. Quick Read submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Is it possible to make an AI-driven system that predicts gearbox failure on a simple $4 MCU? How to automatically build a compact model that does not require any additional compression? Can a non-data scientist implement such projects successfully? I will answer all these questions in my new project. In industry (e.g., wind power, automotive), gearboxes often operate under random speed variations. A condition monitoring system is expected to detect faults, broken tooth conditions and assess their severity using vibration signals collected under different speed profiles. Modern cars have hundreds of thousands of details and systems where it is necessary to predict breakdowns, control the state of temperature, pressure, etc.As such, in the automotive industry, it is critically important t…  ( 5 min )

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https://mixed-news.com/en/artificial-intelligence-china-researches-brain-scale-ai/ From the article: In China, the state and companies are researching AI models with trillions of parameters. They want to prove that they can develop “brain-scale” AI. ... In a new paper, researchers from Tsinghua University, Alibaba Group, Zhejiang Lab and Beijing Academy of Artificial Intelligence present BaGuaLu, a framework that enables the training of large AI models using the Mixture-of-Experts (MoE) architecture. In an initial test, the researchers trained a 1.93 trillion model with their framework, outperforming Google’s Switch Transformer. They also demonstrate that their framework enables models with 14.5 trillion and a full 174 trillion parameters. ... The team expects that giant multimodal AI models could have far-reaching implications for numerous AI applications. submitted by /u/Sephirio [link] [comments]  ( 1 min )

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Google researchers created JAX to conduct NumPy computations on GPUs and TPUs. DeepMind uses it to help and expedite its research, and it is increasingly gaining popularity. Differentiation with grad(), vectorization with map(), and JIT-compilation (just-in-time) with jit are some of the composable functions required for machine learning research in JAX (). As a result, adding a JAX-based workload to the Flower code samples is a must-have. The combination of JAX and Flower allows ML and FL researchers to employ the deep learning framework that their projects demand. The updated code example now serves as a template for migrating existing JAX projects to a federated environment. It’s pretty simple to put up a centralized machine learning architecture, and the JAX developer documentation has multiple examples. Because the ML model parameters are stored in the DeviceArray data format, setting up the federated workload requires some knowledge of JAX. To be compatible with the Flower NumPyClient, those arguments must be converted to NumPy ndarrays. The JAX meets Flower example below demonstrates how a Flower setup might work. Continue Reading submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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Hi all, I have been interested in a field called pattern theory for some time now. Its a mathematical formalism to describe patterns in the world, as well as a framework for developing ai. As far as I can tell, pattern theory seems to be somewhat of a dead field. I'm not sure who is possibly still thinking about it other than a single digit number of academics (e.g. David Mumford). I find this a bit unfortunate, since while I'm still a bit naïve about the field, I'd like to find people I could talk to about it. Does anyone have any recommendations for where I could find people I could talk to about pattern theory/bounce ideas off of relating to pattern theory? Thanks in advance submitted by /u/patterntheoryacc [link] [comments]  ( 1 min )

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Hi, hope everyone is doing will ​ ​ i am an EE student, i am currently taking a class (my first) in machine learning, also i am enjoying it so much, now we have reached the point where we are studying neural networks.. back propgation, CNN, RNN, Autoencoders, Deep Learning etc.. ​ and my prof wants us to get working with some projects (using neural network) ​ basically he wants us to get some already written code in Gethub (((written with Pytorch))) and understand it, understand the task, the motivation, the structure and the math, and modify it if needed, and implement it, and this will be the project, no one starts from scratch ​ for example: image classification object detection ​ also he said that the code that identify numbers is simple for a project, it will be an assignment. ​ so any ideas? or links or any advice in general ​ thanks guys and all the best submitted by /u/Torvaldz_ [link] [comments]  ( 1 min )

Today, customers interact with brands over an increasingly large digital and offline footprint, generating a wealth of interaction data known as behavioral data. As a result, marketers and customer experience teams must work with multiple overlapping tools to engage and target those customers across touchpoints. This increases complexity, creates multiple views of each customer, and […]  ( 10 min )

This is blog post is co-written by Theresa Cabrera Menard, an Applied Scientist/Geographic Information Systems Specialist at The Nature Conservancy (TNC) in Hawaii. In recent years, Amazon and AWS have developed a series of sustainability initiatives with the overall goal of helping preserve the natural environment. As part of these efforts, AWS Professional Services establishes […]  ( 11 min )

Amazon SageMaker Autopilot helps you complete an end-to-end machine learning (ML) workflow by automating the steps of feature engineering, training, tuning, and deploying an ML model for inference. You provide SageMaker Autopilot with a tabular data set and a target attribute to predict. Then, SageMaker Autopilot automatically explores your data, trains, tunes, ranks and finds […]  ( 10 min )

“I am a visionary,” says an AI, kicking off the latest installment of NVIDIA’s I AM AI video series. Launched in 2017, I AM AI has become the iconic opening for GTC keynote addresses by NVIDIA founder and CEO Jensen Huang. Each video, with its AI-created narration and soundtrack, documents the newest advances in artificial Read article > The post Latest ‘I AM AI’ Video Features Four-Legged Robots, Smart Cell Analysis, Tumor-Tracking Tech and More appeared first on NVIDIA Blog.  ( 3 min )

When Tanish Tyagi published his first research paper a year ago on deep learning to detect dementia, it started a family-driven pursuit. Great-grandparents in his family had suffered from Parkinson’s, a genetic disease that affects more than 10 million people worldwide. So the now 16-year-old turned to that next, together with his sister, Riya, 14. Read article > The post Teens Develop Handwriting-Recognition AI for Detecting Parkinson’s Disease appeared first on NVIDIA Blog.  ( 3 min )

I mentioned smoothed step functions in the previous post. What would you do if you needed to concretely use a smoothed step function and not just know that one exists? We’ll look at smoothed versions of the signum function sgn(x) = x / |x| which equals -1 for negative x and +1 for positive x. […] Smoothed step function first appeared on John D. Cook.  ( 2 min )

Partitions of unity are a handy technical device. They’re seldom the focus of attention but rather are buried in the middle of proofs. The name sounds odd, but it’s descriptive. A partition of unity is a set of smooth functions into the interval [0, 1] that add up to 1 at every point. The functions […] Partitions of unity, smooth ramps, and CW clicks first appeared on John D. Cook.  ( 2 min )

Associate professor and principal investigator with the MIT Schwarzman College of Computing’s Science Hub discusses the future of robotics and the importance of industry-academia collaborations.  ( 5 min )

MIT AI Hardware Program launches with five inaugural companies to advance AI technologies for the next decade.  ( 5 min )

Inventory management is an essential part of any eCommerce business. Especially if you are an eCommerce business owner juggling multiple sales channels, it can save you a lot of effort. However, manually managing your inventories is also a recipe for error. Also, let’s not forget the time you have to spend and the painful process… Read More »Automated Inventory Management System: An Ultimate Guide for 2022 and Beyond The post Automated Inventory Management System: An Ultimate Guide for 2022 and Beyond appeared first on Data Science Central.  ( 5 min )

Statistics gives business owners the freedom to evaluate how their websites are performing. The evaluation involves a couple of things: the bounce rate and the exit rate. But what is the difference between bounce rate and exit rate? This is a point of discussion that requires you to have an open mind to grasp the… Read More »What is the Difference Between Bounce Rate and Exit Rate? The post What is the Difference Between Bounce Rate and Exit Rate? appeared first on Data Science Central.  ( 5 min )

There is no denying the importance of the internet and IT in the business scene. Businesses hailing from all sectors are dependent on the web, and they also make use of various types of software applications nowadays. However, with time, such technologies are also evolving. Businesses are coping with huge amounts of data, and to… Read More »Five Major Benefits That Microsoft Power BI Brings To Data Scientists The post Five Major Benefits That Microsoft Power BI Brings To Data Scientists appeared first on Data Science Central.  ( 5 min )

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Hi! I'm currently using garage to simulate a robot EE controlled in position. Does anyone know how to make a relative action? I mean, I would like to have a small action space reinitialized at each step in order to have only small increases in position. Thanks! submitted by /u/Big-Picture8323 [link] [comments]  ( 1 min )

I am currently build a PPO agent for OpenAI gym's Pong environment using Pytorch and I had a question: So typically the workflow is: Use the state as the input for a CNN and output the action probabilities Sample the action distribution for an action and perform env.step(action) with it Obtain there rewards and calculate some reward / loss function such as log probs*rewards Backpropogate this loss/reward using reward/loss.backward() and optimizer.step() Now, Pytorch's loss.backward() and optimizer.step() only calculates and updates gradients for pyTorch Variable objects where requires_grad=True. So how does Pytorch backpropogate through env.step() ? env.step() outputs numpy arrays (if you're using a parallel environment) or integers (not tensors)... Secondly, if I try to convert a Tensor output to a numpy array to input to env.step() - say an array of actions for parallel environments, it breaks my backpropogation right? Thirdly, does that mean that env.step() is a differentiable function? Thanks in advance! submitted by /u/Ska82 [link] [comments]  ( 1 min )

Can someone please explain what "pw" is on the third image(marked with arrow) ? P.S: First two images are for context Thank you! ​ https://preview.redd.it/v9ogmrxvn9q81.png?width=753&format=png&auto=webp&s=4ecfc49aabbf328ecccf468ee01aa3767a7a4c8a https://preview.redd.it/4mrnjsxvn9q81.png?width=753&format=png&auto=webp&s=42be60ebaed04468d1b10928ba729327a0057b0f https://preview.redd.it/o01n5sxvn9q81.png?width=753&format=png&auto=webp&s=112e876c252e075829c1693cf0f6a5d4751cf79a submitted by /u/Whole_Run_4485 [link] [comments]

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A friend and I are thinking about building a physical perceptron as a summer project. However, I cannot find any resources on the physical implementation of the perceptron since Rosenblatt's in the 40's. Does anyone do this? What are some good resources? submitted by /u/HoldDoorHoldor [link] [comments]

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Now unlimited uses for anyone: https://botbox.dev/generator submitted by /u/Recent_Coffee_2551 [link] [comments]

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Hello, i'm trying to reproduce the Decision Transformer paper, however i feel seriously lost on how to do it. I find no documentation on fine-tuning models and have no idea how to use the datasets. Any help would be much appreciated, thanks. submitted by /u/PM_ME_FREE_GAMES [link] [comments]

Hey everybody, around half a year ago, I created a simple job aggregator called datajoblist.com, which fetches/scrapes remote jobs in data science, data engineering and AI from multiple sources and presents them in a simple, unified interface. The jobs are collected both directly from interesting (to me) companies like Stripe or Shopify, as well as filtered from job boards such as weworkremotely.com. I have not touched the site since when I first built it half a year ago, but it seems that people are finding it helpful, as it is now getting rather stable lower few thousand unique visitors per month, and has facilitated thousands of “apply” click-throughs to company sites. A few dozen people even signed up for the mailing list. So, I was thinking about investing a little more time now and adding some improvements. Is there any information/functionality that you would like to see there? Shortly, I will be adding the possibility to post jobs for a small fee (till now, all jobs on the site have been aggregated from elsewhere), but would love to add some usability improvements that are reasonably simple to implement for me. (Perhaps salary ranges, where available?) Thanks for any feedback and have a great day! submitted by /u/k_kristian [link] [comments]  ( 1 min )

I often here the success of neural networks attributed to their status as universal approximators, but there are many algorithms that are universal approximators. For example, decision trees can also be universal approximators, but they don't seem to have nearly as much success. Why is this? What do neural networks have beyond just being universal approximators that makes them special? ​ Is this a question that is currently well understood or is the answer to this question still an area of research? submitted by /u/029187 [link] [comments]  ( 3 min )

Many articles online say GNNs suffer from over-smoothing because nodes aggregate their neighbors and many nodes share similar sets of neighbors. However, in CNN, each pixel also aggregates its neighbors. But CNN can still perform well on some pixel-level classification tasks such as segmentation. submitted by /u/AirZealousideal1342 [link] [comments]  ( 1 min )

https://youtu.be/gYxJEd3EUKs Large language models such as GPT-3 have enabled many breakthroughs and new applications recently, but they come with an important downside: Training them is very expensive, and even fine-tuning is often difficult. This paper presents an adaptive method to improve performance of such models after deployment, without ever changing the model itself. This is done by maintaining a memory of interactions and then dynamically adapting new prompts by augmenting them with memory content. This has many applications, from non-intrusive fine-tuning to personalization. ​ OUTLINE: 0:00 - Intro 0:40 - Sponsor: Introduction to GNNs Course (link in description) 1:30 - Paper Overview: Improve GPT-3 after deployment via user feedback 5:30 - Proposed memory-based architecture 13:00 - A detailed look at the components 15:00 - Example tasks 24:30 - My concerns with the example setup 26:20 - Baselines used for comparison 29:50 - Experimental Results 34:20 - Conclusion & Comments ​ Paper: https://arxiv.org/abs/2201.06009 Code & Data: https://github.com/madaan/memprompt submitted by /u/ykilcher [link] [comments]  ( 1 min )

Many recommendation systems start with a few very popular items that were heavily marketed. The rest of the item list is largely unexplored. How do recommender systems get around this bias and "test" out new items on users to develop richer training data? I could see how a multi-arm bandit might fix this problem but I'd love to hear other ideas and lessons learned. submitted by /u/Shap177 [link] [comments]  ( 1 min )

https://github.com/minimaxir/imgbeddings Instead, this package uses an ONNX INT8-quantized version of CLIP's Vision layers, which in testing works just as well, with a significant performance boost. The demos also turned out very well, and try to a bit more fun than usual. submitted by /u/minimaxir [link] [comments]  ( 1 min )

Hey there, We’re happy to announce that Edward Beeching from Hugging Face has integrated Decision Transformers an Offline Reinforcement Learning method, into the 🤗 transformers library and the Hugging Face Hub. In addition, we share nine pre-trained model checkpoints for continuous control tasks in the Gym environment. If you want to know more about Decision Transformers and how to start using it, we wrote a tutorial 👉 https://huggingface.co/blog/decision-transformers We would love to hear your feedback about it, Thanks, submitted by /u/cranthir_ [link] [comments]  ( 1 min )

Hi fellow nerds, was wondering if anyone has trained the same catboost model on the same dataset in Python and C++ to see which is quicker. Also posting in case someone knows why one language may be inherently quicker. I assume that they are the same program with the same run time but I can’t be too sure of that. Thanks. submitted by /u/econ1mods1are1cucks [link] [comments]  ( 1 min )

​ Vision Transformer Cookbook ​ Hello, I have released the Vision Transformer Cookbook with Tensorflow ! Therefore, you can easy to use the 22 transformer architectures via just copy & paste. I hope this repository would help many people, including tensorflow users. Thank you. ​ * code: vit-tensorflow submitted by /u/taki0112 [link] [comments]  ( 1 min )

I am using Random Forest Regression on a power vs time data of an experiment that is performed for a certain time duration. Using that data, I want to predict the trend of power in future using time as an input. The code that has been implemented is mentioned below. The data set consists of approximately 30 hours of power vs time values as mentioned below. Only active power and time_h columns are used in the algorithm. ​ Data set used for modelling # Creating X and y X = np.array(series[['time_h']]).reshape(-1,1) y = np.array(series['active_power']) # Splitting dataset in training and testing X_train2,X_test2,y_train2,y_test2 = train_test_split(X,y,test_size = 0.15, random_state = 1) # Creating Random Forest model and fitting it on training data forest = RandomForestRegressor(n_estimat…  ( 2 min )

Basically the title. I work as a data engineer for a company I am also a customer of. From an Ethics in ML point of view: what do you think this implies on my responsibilities? submitted by /u/Bani57 [link] [comments]  ( 1 min )

Hey, I have just published my latest medium article. These days, in deep learning, it is usual to hear about transformers’ outstanding performance on the challenges where other algorithms can not meet our expectations when most of them are based on attention. This article gives you a detailed illustration of the code and mathematics of the four most-used types of attention in the Deep Learning era. https://rezayazdanfar.medium.com/everything-about-attention-family-644747903c60 submitted by /u/rezayazdanfar [link] [comments]  ( 1 min )

t-SNE remains a popular embedding method for visualizing high-dimensional data. However, there is little consensus on how to select hyperparameters such as perplexity, learning rate, and exaggeration to best visualize arbitrary data sets. This work systematically explores t-SNE hyperparameters using almost 700 data sets. We replicate past studies, proving that some t-SNE guidelines generalize beyond their original context. But we find that some guidelines do not appear to generalize. We also show a proof of concept neural network system for featurizing data sets and automatically recommending good t-SNE hyperparameters. Paper: https://osf.io/6t5ax/ Blog: https://twosixtech.com/new-guidance-for-using-t-sne/ submitted by /u/rpgove [link] [comments]  ( 3 min )

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Hey ML Reddit! I just released the initial version of MLbot (https://github.com/thecooltechguy/mlbot): a new open-source tool that I’ve been working on for running distributed ML training jobs in your cloud, with a single command. How it works: In short, it allows you to run your training script in the cloud by simply swapping “python” for “mlbot run”. For example, if ``python train.py … can run your training script locally, then mlbot run --instance-type p3dn.24xlarge --num-nodes 2 train.py … should be able to run your code in the cloud across 2 GPU machines. Since this tool runs entirely inside your cloud environment, you don’t have to transfer your training data to a 3rd party, while having full observability into the underlying infrastructure. Why I built this: In a recent ML pr…  ( 2 min )

Looking for ideas and pointers on how to solve this problem. Dependency parsing? Are there any open source ML models to solve this problem? Googling isn't helping. Made a mistake in the description - we want to explain the negative sentiment of the aspect "tone" (rather than guitar) submitted by /u/ml_guy1 [link] [comments]  ( 1 min )

Hello, I am in the process of finding a suitable conference for my paper, and I find that they usually have a research track and applied research track. One conference defines the applied research track as The Applied Research Track aims at attracting submissions from both industry and academia that either solve or advance the understanding of issues related to deploying AI, Information Retrieval (IR), and big data technologies as part of actual applications. My paper is roughly related to applying deep learning for time series anomaly detection. Should I go for the research track or the applied research track? submitted by /u/mythrowaway0852 [link] [comments]  ( 1 min )

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Hey there 👋🏻, We’re happy to announce that Edward Beeching from Hugging Face has integrated Decision Transformers an Offline Reinforcement Learning method, into the 🤗 transformers library and the Hugging Face Hub. In addition, we share nine pre-trained model checkpoints for continuous control tasks in the Gym environment. If you want to know more about Decision Transformers and how to start using it, we wrote a tutorial 👉 https://huggingface.co/blog/decision-transformers We would love to hear your feedback about it, In the coming weeks and months, we will be extending the reinforcement learning ecosystem by: Being able to train your own Decision Transformers from scratch. Integrating RL-baselines3-zoo Uploading RL-trained-agents models into the Hub: a big collection of pre-trained Reinforcement Learning agents using stable-baselines3 Integrating other Deep Reinforcement Learning libraries Implementing Convolutional Decision Transformers for Atari And more to come 🥳, so 📢 The best way to keep in touch is to join our discord server to exchange with us and with the community. Thanks, submitted by /u/cranthir_ [link] [comments]  ( 1 min )

Assume that the time between two agent actions is not fixed, i.e. depending on the state-action, the agent can become unavailable for a time t = t(s, a). During the time the agent is unavailable, several rewards are produced by the environment, and they need to be given to the agent whenever it becomes available again. One easy way to deal with this is to just store them and set the reward at the next available state as the sum of the accumulated rewards. But in the discounted reward framework with temporal difference (e.g. DQN) this does not discount rewards properly. How can I set the reward for the next state such that it contains all the accumulated rewards but it is correct in the DQN setting? submitted by /u/fedetask [link] [comments]  ( 2 min )

What are the current best algorithms in Reinforcement Learning? It seems everyone still uses TD3, SAC, PPO, Rainbow DQN, etc. However, these are mostly from 2018, which is old for RL standards. What happened afterwards? What is the current algorithm for these kinds of standard tasks? I'm especially interested in algorithms that can handle continuous action spaces. Thank you very much! submitted by /u/Paraiso93 [link] [comments]  ( 2 min )

i'm reading the Sutton-Barto and at page 63 it is written then v_star(s)=max_a q_star(a,s), my question is why we have this? where does it come from? i'm trying to start from the definition of v_star and q_star but I can't really find a way submitted by /u/samas69420 [link] [comments]  ( 2 min )

We are in the middle of a business model revolution.  And we are active participants in that revolution.  We have been transitioning from a society where possession and application of physical commodities defined wealth and power, to a society where possession and application of knowledge define wealth and power.  Throughout the 20th century, oil had… Read More »What’s Your Business Model Choice – Hammers or Casino? The post What’s Your Business Model Choice – Hammers or Casino? appeared first on Data Science Central.  ( 7 min )

It is common for growing organizations to reach a point where their existing data solution is no longer adequate for their needs. In most cases, it happens with companies that have used an on-premises infrastructure from the earliest days of business but now need to upgrade their network for continued growth. However, relocating equipment and… Read More »Datacenter relocation is now easier, faster, and more affordable The post Datacenter relocation is now easier, faster, and more affordable appeared first on Data Science Central.  ( 3 min )

Astronomy has seen an exponential rise in data collection over the last decade. This requires new methods for data analysis, including AI. With the launch of new surveys, big data methodology has become a necessity. A new class of extremely large telescopes has evolved to collect vast amounts of data; The volume of data collected… Read More »The Evolution of Astronomical AI The post The Evolution of Astronomical AI appeared first on Data Science Central.  ( 4 min )

When such a sophisticated, risky, and complex technology like AI takes our lives by storm, a clearly defined set of rules on its usage is paramount. Previously, public concern was mostly focused on the inappropriate use of personal data. As AI becomes a key technology in many businesses and services, the attention is rightfully shifting… Read More »What to Do About the New AI Regulation? The post What to Do About the New AI Regulation? appeared first on Data Science Central.  ( 4 min )

From chatbots and other remote helpers to producing content, improving client encounters, AI companies are now rolling out significant improvements to the advanced promoting scene. While it might be hard to foresee what’s to come, it’s not difficult to see that AI will proceed to advance and assume an undeniably focal point in computerized advertising.… Read More »How Automation and AI Are Changing Internet Marketing The post How Automation and AI Are Changing Internet Marketing appeared first on Data Science Central.  ( 4 min )

What is Data Science? Data science is a study that helps us to extract information from a set of structured or unstructured data. It makes use of the study of statistics, mathematics, scientific computation to analyze the data.  Demand for Python in Data Science: Before we deep dive into the topic let’s firstly discuss why… Read More »How Python Became THE Language for Data Science The post How Python Became THE Language for Data Science appeared first on Data Science Central.  ( 7 min )

In today’s landscape, businesses need to look for any competitive advantage they can to ensure their survival, growth and success. A key aspect of gaining a competitive advantage is using data-driven insights to empower decisions for marketing, consumer insights, consumer segmentation, and operations, such as merchandising and real estate.  Especially within large companies, it is… Read More »Three Critical Steps for Data-Driven Success The post Three Critical Steps for Data-Driven Success appeared first on Data Science Central.  ( 4 min )

Data governance is the management of organizations’ data availability, usability, integrity, security, and privacy. According to Gartner, Data governance is the specification of decision rights and a framework for accountability to assure acceptable behavior in the value, generation, consumption, and control of data and analytics. Why Do Organizations Need It? It ensures that data is consistent,… Read More »Data Governance Tool: What To Look For? The post Data Governance Tool: What To Look For? appeared first on Data Science Central.  ( 3 min )

Introduction Security is the buzzword for the digital world today. Businesses have realized that thriving and surviving without a well-functioning security system in place is tough. Security breaches, malware, ransomware and similar incidents are real. The businesses that have suffered from malicious attacks very well know how grave these attacks can be.  The importance of… Read More »Top MDM-Enabled Data Security Hacks You Should Know About The post Top MDM-Enabled Data Security Hacks You Should Know About appeared first on Data Science Central.  ( 5 min )

“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.  ( 6 min )

Suppose you start with some large number x and want to find a prime number at least as big as x. First you test whether x is prime. Then you test whether x + 1 is prime. Then you test whether x + 2 is prime, and so on until you find a prime. Of […] Looking for the next prime first appeared on John D. Cook.  ( 3 min )

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If you are running a business today using Agile methods, it’s likely that you are not getting the productivity boost from it that you should, and your time to market for new features is probably not what it could be either. Is that the end of the world?  By and large, yes!  The problem is… Read More »Agile, Agile 2 and Agility, Part I The post Agile, Agile 2 and Agility, Part I appeared first on Data Science Central.  ( 5 min )

The dynamics of the global commercial artificial intelligence market continues to change over time, thanks to the persistent advancements in technology. This research report offers a detailed and insightful assessment of the global commercial artificial intelligence market, taking primary trends and the future prospects of this market in consideration. Various segments of this market, based on a… Read More »Commercial Artificial Intelligence — The Future of BI The post Commercial Artificial Intelligence — The Future of BI appeared first on Data Science Central.  ( 3 min )

One of the best known examples of GPT-3 for developers is the Github co-pilot Trained on billions of lines of public code, GitHub Copilot is more than autocomplete of code. GitHub Copilot is powered by Codex, the new AI system created by OpenAI. GitHub Copilot understands significantly more context than most code assistants. GitHub Copilot… Read More »GitHub Co-Pilot Alternatives: Can They Match the Functionality of Co-Pilot? The post GitHub Co-Pilot Alternatives: Can They Match the Functionality of Co-Pilot? appeared first on Data Science Central.  ( 3 min )

Increasingly, companies are focused on finding ways to connect to new and valuable sources of data in order to enhance their analytical capabilities, enrich their models, or deliver more insight to their business units.  Due to the increased demand for new data sources, companies are also looking at their internal data differently. Organizations that have… Read More »Five Key Components of a Data Sharing Platform The post Five Key Components of a Data Sharing Platform appeared first on Data Science Central.  ( 7 min )

Despite the technological breakthroughs in the advent of Industry 4.0, manufacturers seem to have taken a more gradual approach to adoption. In 2020, less than 30 percent of the industry considered themselves extensive users of advanced integrated tools and processes. The pandemic, however, brought out an unprecedented need to explore opportunities that make manufacturing systems… Read More »Smart Maintenance – How SaaS Frameworks Turn Insights Into Actions Quickly And Efficiently The post Smart Maintenance – How SaaS Frameworks Turn Insights Into Actions Quickly And Efficiently appeared first on Data Science Central.  ( 5 min )

What is the toll-free number? Businesses provide a cloud-based contact number to allow customers to contact them free of cost. In India, this number- the business toll-free number is available in the 1800 series in an easily recognizable format- 1800-ABC-DEFG. Customers do not have to incur any fee to contact the business, as the company… Read More »Toll-free number: What is it, and how can you get one for your business? The post Toll-free number: What is it, and how can you get one for your business? appeared first on Data Science Central.  ( 4 min )

With the exponential growth in the number of big data applications in the world, Testing in big data applications is related to database, infrastructure and performance testing, and functional testing. The advancement of technology is enabling the collection of a massive amount of data almost every second. And, big data has emerged as the buzzword… Read More »Top Strategies and Best Practices for Big Data Testing The post Top Strategies and Best Practices for Big Data Testing appeared first on Data Science Central.  ( 3 min )

I had heard of the Svalbard Global Seed Vault, but I hadn’t heard of the nearby Arctic World Archive until today. The latter contains source code preserved on film, a format that should last at least 500 years. Seed vault, but for code first appeared on John D. Cook.  ( 1 min )

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Deep Neural Networks (DNNs) have excelled at solving complex real-world problems, however, training a good DNN has become more complex. It is challenging to ensure that the optimizers used will converge to reliable minima with acceptable model performance when only minimizing the conventional empirical loss. Tsinghua University’s research team proposes Stochastic Scheduled SAM (SS-SAM), a novel and effective DNN training strategy. In SS-SAM, the optimizer is set up by a predetermined scheduling function to run a random trial at each update step, which selects whether to perform the SGD or SAM optimization at random. The overall number of propagation pairs could be significantly decreased in this approach. The team’s approach provides equivalent or higher model training performance at a lower computational cost than baseline sharpness-aware minimization (SAM). Continue Reading Paper: https://arxiv.org/pdf/2203.09962.pdf submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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As in...get the voice down from improving an A.I recording. Then, give it some basic code or responses that you had at different ages. Also, getting a bunch of 3D images of yourself. Then coding it with some basic "values" and creating some sort of generic conditional statement for the 3 basic values that it has that match yours. Then, over time, actually diving into artificial intelligence and slowly updating and replacing those to continue improving it to match you (and your growth)? Hmmm, I wonder if there would be a way to preserve it. Everything changes (like sites -> VR and so on). So some sort of "survival" instinct (which seems impossible to code but would be fun to try undertaking). submitted by /u/the_evil_intp [link] [comments]  ( 2 min )

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DeepMind released AlphaFold 2 last year, which made headlines for its incredible accuracy in protein structure prediction. The success of AlphaFold demonstrated that deep neural networks might be used to solve challenging and critical structural biology problems. FastFold is a highly effective protein structure prediction model formulation for training and inference developed by a group of researchers from the National University of Singapore. Although AlphaFold 2 is a game-changer in protein structure prediction, training and inference remain time-consuming and costly. This is something that the study team is concerned about. Continue Reading This Article Here Paper: https://arxiv.org/pdf/2203.00854v1.pdf Github: https://github.com/hpcaitech/FastFold submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Deep Neural Networks (DNNs) have excelled at solving complex real-world problems, however, training a good DNN has become more complex. It is challenging to ensure that the optimizers used will converge to reliable minima with acceptable model performance when only minimizing the conventional empirical loss. Tsinghua University’s research team proposes Stochastic Scheduled SAM (SS-SAM), a novel and effective DNN training strategy. In SS-SAM, the optimizer is set up by a predetermined scheduling function to run a random trial at each update step, which selects whether to perform the SGD or SAM optimization at random. The overall number of propagation pairs could be significantly decreased in this approach. The team’s approach provides equivalent or higher model training performance at a lower computational cost than baseline sharpness-aware minimization (SAM). Continue Reading Paper: https://arxiv.org/pdf/2203.09962.pdf submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

As you can see in the figure, i'm passing the variable data_inputs to the feedforward_comp function over iterations. Up to epoch = 9, everything computes fine but after that, data_inputs suddently is being passed as empty? Can someone please tell me why this happens and how to fix it? https://preview.redd.it/2wds226ktup81.png?width=816&format=png&auto=webp&s=61c55db011708e2d2b373a0b4e4b02355ec50730 submitted by /u/lwhisper [link] [comments]  ( 1 min )

Hey guys, I really need your help on this one. I go to university next year and I will be studying computer engineering which means there will be a lot of coding going into it. Now I have 4 options for laptop First : INSPIRON 15.6" INTEL CORE 17-1165G7 TOUCHSCREEN 2-IN-1 LAPTOP Second: GALAXY BOOK PRO 360 15" 2-IN-1 INTEL 17 LAPTOP Third: HP 15.6" Touchscreen 2-in-1 Laptop - Nightfall Black (AMD Ryzen 7 5700U/1TB SSD/16GB RAM/Windows 10) Fourth: HP Pavilion x360 15.6" Touchscreen 2-in-1 Laptop - Silver (Intel Core i7-1165G7/1TB SSD/16GB RAM/Win 11) Please and thank you guys :) submitted by /u/Traditional-Cow47 [link] [comments]  ( 2 min )

4 years ago, I posted here to introduce some work I did using AI for breast tumor detection and classification: https://www.reddit.com/r/MachineLearning/comments/8rdpwy/pi_made_a_gpu_cluster_and_free_website_to_help/ That post gain some traction on Reddit and I hope you would like the one I am gonna introduce here again. In the recent years, I have been shifting my focus from cancer detection to the actual treatment. One particular problem we really want to solve is to have more brain cancer patients to be accessible to stereotactic radiosurgery (SRS) which has a lot better treatment outcome and much better quality of life (QoL) for the patient than whole brain radiotherapy (WBRT) which is more common for patients with multiple brainiest (say more than 5 or 10). The reason behind …  ( 2 min )

Hey guys, I'm currently dealing with my bachelor degree's final project. But my pc seems to be slow and it gets really hot + I think it might be dying, I really need to send it to the technical service. :( Well, I'm not familiar with other cloud services like Azure or AWS but I used Google Colab a lot, and right at this moment I also use it. But it's constantly asking if I'm "there". It always wants an interaction otherwise it shutdowns the session and my time gets wasted, just gotta do everything from the start. So if I pay for the colab pro (unpluss) version, will my experience get better? Will I need to interact with colab every hour again? Or should I consider other alternatives? submitted by /u/average_turanist [link] [comments]  ( 1 min )

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Previous weeks : 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 Week 1 Week 11 Week 21 Week 31 Week 41 Week 51 Week 61 Week 71 Week 81 Week 91 Week 101 Week 111 Week 121 Week 131 Week 2 Week 12 Week 22 Week 32 Week 42 Week 52 Week 62 Week 72 Week 82 Week 92 Week 102 Week 112 Week 122 Week 132 Week 3 Week 13 Week 23 Week 33 Week 43 Week 53 Week 63 Week 73 Week 83 Week 93 Week 103 Week 113 Week 123 Week 133 Week 4 Week 14 Week 24 Week 34 Week 44 Week 54 Week 64 Week 74 Week 84 Week 94 Week 104 Week 114 Week 124 Week 5 Week 15 Week 25 Week 35 Week 45 Week 55 Week 65 Week 75 Week 85 Week 95 Week 105 Week 115 Week 125 Week 6 Week 16 Week 26 Week 36 Week 46 Week 56 Week 66 Week 76 Week 86 Week 96 Week 106 Week 116 Week 126 Week 7 Week 17 Week 27 Week 37 Week 47 Week 57 Week 67 Week 77 Week 87 Week 97 Week 107 Week 117 Week 127 Week 8 Week 18 Week 28 Week 38 Week 48 Week 58 Week 68 Week 78 Week 88 Week 98 Week 108 Week 118 Week 128 Week 9 Week 19 Week 29 Week 39 Week 49 Week 59 Week 69 Week 79 Week 89 Week 99 Week 109 Week 119 Week 129 Week 10 Week 20 Week 30 Week 40 Week 50 Week 60 Week 70 Week 80 Week 90 Week 100 Week 110 Week 120 Week 130 Most upvoted papers two weeks ago: /u/CatalyzeX_code_bot: Paper link /u/PaganPasta: https://arxiv.org/abs/2105.05233 Besides that, there are no rules, have fun. submitted by /u/ML_WAYR_bot [link] [comments]  ( 1 min )

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Saw this comment in a Q&A on big deep learning models. This seems to have side-effects both good and bad. The prediction, if true, forces accessibility (though expensive) but also creates silos. First time poster. What do you guys think? https://m12.vc/news/direct-line-with-saurabh-tiwary-whats-next-for-large-foundational-models The economics are making it untenable for most people except the most well-funded organizations to invest in large language models. I will make the comparison to the semiconductor ecosystem. If you look at fabrication economics for semiconductor chips, they cost tens to hundreds of millions of dollars and have relatively short lifetimes. One needs very large volume usage to justify manufacturing a custom ASIC (Application Specific Integrated Circuits). Thus, we do not have that many companies fabricating chips. However, we have an entire software and systems eco-system which relies on these chips that have built massive industries around them. And, if you look at the biggest companies in the world (maybe, except Apple), they have very little to do with ASIC design and fabrication as part of their core business. I think a similar eco-system would pan out in the large-scale modeling space as well. We would have a few well-funded companies that would be training these extremely large and reusable models and other companies would build applications and services reusing and customizing these models. submitted by /u/SufficientActive8895 [link] [comments]  ( 1 min )

I've written a short blog post on modern data augmentation techniques. Please have a read and provide feedback. I've explained Cutout, Mixup, CutMix and Label smoothing with code and examples. https://pmgautam.com/augmentations/2022/03/27/Augmentations-visually-explained.html submitted by /u/p1g1 [link] [comments]  ( 2 min )

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 1 min )

We added author pages, which lists all the papers by the author and links to their social and academic web pages. E.g.: https://papers.labml.ai/author/39815586a03711ecbb8c3d25c114d5ed https://papers.labml.ai/author/56b63a47a03711ecbb8c3d25c114d5ed Highlights, Links to Google and Arxiv searches. Sort papers based on the published date and the popularity in Twitter. Links to Twitter, Google Scholar, Github and Linkedin etc if available in our database. We love to hear your feedback and suggestions. Thank you all, and I appreciate the support. submitted by /u/hnipun [link] [comments]  ( 1 min )

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I am learning RL by having the algorithm play Jump King and am streaming it on twitch, having the chat play against it as well to see who can get the babe first. Check it out at: https://www.twitch.tv/unassignedseat submitted by /u/UnassginedSeat [link] [comments]  ( 1 min )

Hello all, I have a question regarding optimizing a policy represented by a neural network. In Supervised Learning, the intermediate activations created during the forward pass are needed during backpropagation in order to compute weight gradients. This has led to a number of memory management techniques such as offloading and checkpointing being created. My question is whether the same is true in DRL. For policy-gradient methods for example, learning starts from an objective computed from the trajectory such as the discounted returns, but are the intermediate activations created during action inference needed when optimizing the policy (i.e. learning)? Is there any academic source that covers this topic? Thanks! submitted by /u/PSylvan [link] [comments]  ( 1 min )

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My goal is to determine the best course of actions to take given a certain state. I'm working in some state space X. For every x in X, I can assign it a value. When I perform an action a given x, I map x to some new state x'. The state x' depends on my action up to some noise produced by the environment. I think this is a reinforcement learning problem. What methods are suitable in this context? submitted by /u/heylanguage [link] [comments]  ( 1 min )

A brief overview of the game: A deck of 50 objects are chosen from a set of ~1000 objects. The game is then played out deterministically, and rewards are dished out based on win/loss. I would like to build a nn that can produce good decks , trained using self-play. However, I'm not too sure how to approach this problem. Relevant research or pointers would be very helpful. Thanks. submitted by /u/nutpeabutter [link] [comments]  ( 1 min )

In real-time forecasting in public health, data collection is a non-trivial and demanding task. Often after initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches to a stable value. This so-called 'backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. In this paper, we introduce the multi-variate backfill problem using COVID-19 as the motivating example. We construct a detailed dataset composed of relevant signals over the past year of the pandemic. We then systematically characterize several patterns in backfill dynamics and leverage our observations for formulating a novel problem and neural framework Back2Future that aims to refines a given model's predictions in real-time. Our extensive experiments demonstrate that our method refines the performance of top models for COVID-19 forecasting, in contrast to non-trivial baselines, yielding 18% improvement over baselines, enabling us obtain a new SOTA performance. In addition, we show that our model improves model evaluation too; hence policy-makers can better understand the true accuracy of forecasting models in real-time.  ( 3 min )

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Love how my keyboard konked out. So I recently read about megasyn the AI being used to create 40k new weapons in 6 hours. This got me wondering. Can AI be made to create different enamels and paints for things like pottery glazes, cloisonne enamels and paints for art work? If so how would one go about doing this knowing literally nothing? submitted by /u/Grendal87 [link] [comments]  ( 1 min )

Found a useful list of Tools and Programs for AI/ML. Looks like it covers Machine Learning, Deep Learning, Computer Vision(CV), and Natural Language Processing (NLP). I thought I'd share it for anyone that's interested. https://github.com/mikeroyal/Machine-Learning-Guide submitted by /u/Khaotic_Kernel [link] [comments]

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I thought it will be quite interesting to see Deep Convolutional GAN’s capability in generating wildlife, so I wrote a tutorial on how to build a model based on the DCGAN architecture through PyTorch: https://taying-cheng.medium.com/create-new-animals-using-dcgan-with-pytorch-2ce47810ebd4 submitted by /u/Ok-Peanut-2681 [link] [comments]

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When making zero-shot inference, large pre-trained models like CLIP or ALIGN provide consistent accuracy across various data distributions (i.e., without fine-tuning on a specific dataset). While existing fine-tuning methods vastly improve accuracy on a given target distribution, they frequently compromise robustness to distribution shifts. This conflict can be resolved by presenting a simple and effective strategy for enhancing robustness while fine-tuning: assembling the zero-shot and fine-tuned models (WiSE-FT). An approach for fine-tuning AI models that enhance robustness during distribution shift has been open-sourced by researchers from the University of Washington (UW), Google Brain, and Columbia University. According to tests, WISE-FT improves accuracy by up to 6% on specific computer vision (CV) benchmarks. Continue Reading The Research Summary Article Here Paper: https://arxiv.org/pdf/2109.01903.pdf Github: https://github.com/mlfoundations/wise-ft https://preview.redd.it/1ltzmg5iwnp81.png?width=2803&format=png&auto=webp&s=6c0727432072b67c1723838e3097ec901f34b1c0 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Hey, I am wondering about the losses of conditional GANs, particularly their magnitude. Due to the amount of classes/identities, the classification loss will usually be significantly higher than discrimination loss (data identified as real or generated). When using traditional multitask learning where losses are simply summed up, how is the generator supposed to learn to generate realistic-appearing data when the loss that would encourage that is so low in comparison to the classification loss? submitted by /u/Timboron [link] [comments]  ( 1 min )

Hi, does anyone has experience in augmentation for GANs? Especially for Cycle-GAN like image to image translation. When I see image-to-image GANs, there is mostly no augmentation applied. submitted by /u/SeucheAchat9115 [link] [comments]  ( 1 min )

I am doing some face recognition and am wondering how does apple's FaceID work. They say it is a "true depth" camera. What does that mean? is that a lidar? some kind of a dot projector? Basically what I want to know is what kind of data does that device provide. Also, is there a commercially available device similar to that true depth camera available? submitted by /u/user89320 [link] [comments]  ( 1 min )

Hi there, ​ I'm trying to train a GAN to generate video game portraits (think Baldur's Gate, Divinity, that kind of stuff). My GAN is trained on 4096 portraits, 128x192 pixels in size. Batch size is 64, 64 features, 128 dim noise vector. ​ After a few epochs of the expected low quality random stuff, my Generator starts generating images where you can kind of imagine silhouettes and faces. Here's after 250 epochs: ​ https://preview.redd.it/afc4i2r6zpp81.png?width=568&format=png&auto=webp&s=f8e386a8da4976a20b8db525914e5944346b9240 But after 500 the results are pretty much the same: ​ https://preview.redd.it/k9ikn9k7zpp81.png?width=568&format=png&auto=webp&s=3ec106e5ef5d54c3a8162bb45941fb3f3f9af4a2 Losses seem be mostly stable fairly quickly too (sorry, lost the graph after I accidentally shutdown my computer - I save the model every 25 epochs but not the losses graph): Epoch 255 Step 16320: Generator loss: 7.4651877045631405, critic loss: -9.97341676712036 ... Epoch 310 Step 19840: Generator loss: 7.401800179481507, critic loss: -9.479909801483156 ... Epoch 511 Step 32720: Generator loss: 11.518763446807862, critic loss: -8.105796337127687 (The 7.5 -> 11.5 GLoss looks pretty much flat on the graph. Maybe the huge initial losses puts it out of perspective?) ​ Is my GAN "stuck" or do I just need to keep training, and quality gains from that point on are going to be slower? submitted by /u/-Anordil- [link] [comments]  ( 1 min )

I'm experimenting with DCGan architecture and tried adding residual blocks to dcgan before each upscaling. The dcgan is trainiing via a WGAN-GP training procedure, and so far, I could nor really get any sensible result. Is there any guideline about how skip connections should be implemented in GANs? I'm using a very normal resnet type skip connection. The FID score keeps worsening since the start of training, and I've tweaked a lot of hyperparameters, but I still don't have see any improvements. Although I could not train that many epochs because I'm training this on colab. You can find the codes here to see the architecture: Generator and Critic. Another info: The dataset is roughly 100K book covers I've scraped from internet. submitted by /u/feryet [link] [comments]  ( 1 min )

Hi everyone. I am pursuing Masters in Computer Engineering. However, I did my Bachelor's in Computer Science and dabbled with ML a bit. I would like to continue with it during my Masters as well. I am wondering if there is an intersection between the disciplines. If the two fields share considerable overlap, would it be possible to pursue research in ML as a CE graduate? Thanks submitted by /u/RealMatchesMalonee [link] [comments]  ( 1 min )

Machine learning models are statistical, not causal in nature. That means they don’t necessarily hold on intervention. This should make predicting properties of drugs particularly problematic because we are not drawing further testing samples from a distribution that is similar to the training distribution, but we’re completely changing the input as we wish. Why is machine learning/deep learning successful at predicting these properties when the wider research community is struggling to make deep learning models robust, never mind causal, in general. submitted by /u/lemlo100 [link] [comments]  ( 4 min )

State-of-the-art autoregressive image generation model of Kakao Brain ! The paper and codes of "Autoregressive Image Generation using Residual Quantization", which is accepted by CVPR'22, are released. Our study outperforms previous autoregressive image generation models, while increasing the sampling speed upto ~7x faster. In addition, we release RQ-Transformer with 3.9B parameters trained on 30M text-image pairs. To the best of our knowledge, it is the largest text-to-image model among public available models. ​ Examples of Generated Images in the Paper ​ Examples of Generated Images by RQ-Transformer with 3.9B parameters. The model is publicly available now ! Enjoy ! Paper: https://arxiv.org/abs/2203.01941 Code: https://github.com/kakaobrain/rq-vae-transformer submitted by /u/leedoyup [link] [comments]  ( 2 min )

I'm in the process of looking for a good GPU server for my university lab. We have a server room to install the new server. However, while looking into different vendors, I notice that workstations cost less than a rack-mount server even they have better specifications (I saw a gpu workstation with a 48-core 3.8 GHz processor and its cost still a few thousands less than a rack server with similar specifications and the same number and type of GPUs but with a lower grade processor with only 24-core 2.5 GHz). I was really surprised. Is there an advantage for buying a rack-mount GPU server over a GPU Workstation given it cost more for similar or lower specifications? submitted by /u/majax21 [link] [comments]  ( 2 min )

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I recently wrote a critical review on "Deep Learning for Tabular Data" which reviews whether we are ready to move from Tree-based models to Neural Network-based models for Tabular data. It covers many novel approaches such as DeepInsight, IGTD and SuperTML. It also includes some of the transformers based recent works such as TabNet, Tab-Transformer, AutoInt, FT-Transformer and regularisation models such MLP+. I have most commonly found the lack of a defined benchmark which makes it hard for people to find the right algorithms for the task. I am creating this discussion so that people who are using some of these algorithms or have tested some of them in different scenarios can share their findings. submitted by /u/Raghuvansh_Tahlan [link] [comments]  ( 1 min )

In sutton's book, Actor-Critic firstly uses an approximated value function as a baseline, and uses the error to update the policy. In my opinion, the value function is used as a baseline since it assign high value to state with high expected return, and every action in this state have the same baseline. Pseudocode in sutton's book But I also see a Q-version of AC algorithm, which use Q function as the baseline. In this algorithm, the Q function is used to update the policy, and the TD error is used to update the Q function. How could we get this? Q Actor-critic Another question is about AC and A2C. Is expected return (G) minus baseline the same as the advantage function in A2C? If so, is that AC with baseline the same as A2C? submitted by /u/ZavierTi2021 [link] [comments]  ( 1 min )

I am thinking of applying the deep rl on small scale robots. I have an Arduino Uno and some servos, so is it possible that deep rl can be applied using Arduino? submitted by /u/Better-Ad8608 [link] [comments]  ( 1 min )

Hi Guys! I am just starting out in RL and I have a possibly stupid question about deep q-learning. Why do all of the code examples train the model on its own discounted prediction plus the reward, if they could just record all of the rewards in an episode and then calculate the total discounted rewards from the actual rewards the agent got in the episode? At least in my Implementations, the latter strategy seems to outperform the former, both in regard to the time it took the model to converge and the quality of the learned policy. submitted by /u/KayJersch [link] [comments]  ( 2 min )

the docs are light and I understand they're being revamped but I can't find a definition of the outputs for ale. I understand it depends on the specific environment exactly, e.g. for non-atari envs like lunar lander it gives positional data but for the atari games docs state nothing other than its the memory dump. do I treat it like an image without the processing being necessary. can I reshape it into a matrix the size of the raw image output, and throw it into a series of convolutional layers? or do I treat it as positional data like the location of all the objects? submitted by /u/clockface99 [link] [comments]  ( 2 min )

Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window. Most current work uses a fixed single-scale window for modeling by default, ignoring the impact of window size on model performance. However, this may limit the modeling potential of these window-based models for multi-scale information. In this paper, we propose a novel method, named Dynamic Window Vision Transformer (DW-ViT). The dynamic window strategy proposed by DW-ViT goes beyond the model that employs a fixed single window setting. To the best of our knowledge, we are the first to use dynamic multi-scale windows to explore the upper limit of the effect of window settings on model performance. In DW-ViT, multi-scale information is obtained by assigning windows of different sizes to different head groups of window multi-head self-attention. Then, the information is dynamically fused by assigning different weights to the multi-scale window branches. We conducted a detailed performance evaluation on three datasets, ImageNet-1K, ADE20K, and COCO. Compared with related state-of-the-art (SoTA) methods, DW-ViT obtains the best performance. Specifically, compared with the current SoTA Swin Transformers \cite{liu2021swin}, DW-ViT has achieved consistent and substantial improvements on all three datasets with similar parameters and computational costs. In addition, DW-ViT exhibits good scalability and can be easily inserted into any window-based visual transformers.  ( 2 min )

Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy. In this paper, we show how this robustness arises from hedging against worst-case perturbations of the reward function, which are chosen from a limited set by an imagined adversary. Using convex duality, we characterize this robust set of adversarial reward perturbations under KL and alpha-divergence regularization, which includes Shannon and Tsallis entropy regularization as special cases. Importantly, generalization guarantees can be given within this robust set. We provide detailed discussion of the worst-case reward perturbations, and present intuitive empirical examples to illustrate this robustness and its relationship with generalization. Finally, we discuss how our analysis complements and extends previous results on adversarial reward robustness and path consistency optimality conditions.  ( 2 min )

Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, such as which parameters are important on a specific measured outcome or which parameter values we should expect to observe given a set of input parameters. At the same time, however, these models rely heavily on assumptions (e.g., stationarity) or are "black box" (e.g., deep neural networks), meaning that they lack interpretability of their internal working and can be viewed only in terms of their inputs and outputs. An interpretable alternative to the "black box" models and with considerably less assumptions is symbolic regression (SR). SR searches for the optimal model structure while simultaneously optimizing the model's parameters without relying on an a-priori model structure. In this work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at high frequencies through the entire flight, in order to uncover meaningful algebraic relationships between the EGT and other measurable engine parameters. The experimental results exhibit promising model accuracy, as well as explainability returning an absolute difference of 3{\deg}C compared to the ground truth and demonstrating consistency from an engineering perspective.  ( 2 min )

Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for selfsupervised identification of atypical scenes or objects. State-of-the-art augmentation methods arbitrarily embed "structural" peculiarity on typical images so that classifying these artefacts can provide guidance for learning representations for the detection of anomalous visual signals. In this paper, however, we argue that learning such structure-sensitive representations can be a suboptimal approach to some classes of anomaly (e.g., unhealthy fruits) which could be better recognised by a different type of visual element such as "colour". We thus propose Channel Randomisation as a novel data augmentation method for restricting neural networks to learn encoding of "colour irregularity" whilst predicting channel-randomised images to ultimately build reliable fruit-monitoring robots identifying atypical fruit qualities. Our experiments show that (1) this colour-based alternative can better learn representations for consistently accurate identification of fruit anomalies in various fruit species, and also, (2) unlike other methods, the validation accuracy can be utilised as a criterion for early stopping of training in practice due to positive correlation between the performance in the self-supervised colour-differentiation task and the subsequent detection rate of actual anomalous fruits. Also, the proposed approach is evaluated on a new agricultural dataset, Riseholme-2021, consisting of 3.5K strawberry images gathered by a mobile robot, which we share online to encourage active agri-robotics research.  ( 2 min )

Real-world decision-making systems are often subject to uncertainties that have to be resolved through observational data. Therefore, we are frequently confronted with combinatorial optimization problems of which the objective function is unknown and thus has to be debunked using empirical evidence. In contrast to the common practice that relies on a learning-and-optimization strategy, we consider the regression between combinatorial spaces, aiming to infer high-quality optimization solutions from samples of input-solution pairs -- without the need to learn the objective function. Our main deliverable is a universal solver that is able to handle abstract undetermined stochastic combinatorial optimization problems. For learning foundations, we present learning-error analysis under the PAC-Bayesian framework using a new margin-based analysis. In empirical studies, we demonstrate our design using proof-of-concept experiments, and compare it with other methods that are potentially applicable. Overall, we obtain highly encouraging experimental results for several classic combinatorial problems on both synthetic and real-world datasets.  ( 2 min )

Generalized few-shot semantic segmentation was introduced to move beyond only evaluating few-shot segmentation models on novel classes to include testing their ability to remember base classes. While the current state-of-the-art approach is based on meta-learning, it performs poorly and saturates in learning after observing only a few shots. We propose the first fine-tuning solution, and demonstrate that it addresses the saturation problem while achieving state-of-the-art results on two datasets, PASCAL-5i and COCO-20i. We also show that it outperforms existing methods, whether fine-tuning multiple final layers or only the final layer. Finally, we present a triplet loss regularization that shows how to redistribute the balance of performance between novel and base categories so that there is a smaller gap between them.  ( 2 min )

We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing technique and a query execution algorithm with various optimizations. We prove that the proposed query execution algorithm is instance optimal. Experiments with our prototype show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63x and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes.  ( 2 min )

We present a deep neural network for removing undesirable shading features from an unconstrained portrait image, recovering the underlying texture. Our training scheme incorporates three regularization strategies: masked loss, to emphasize high-frequency shading features; soft-shadow loss, which improves sensitivity to subtle changes in lighting; and shading-offset estimation, to supervise separation of shading and texture. Our method demonstrates improved delighting quality and generalization when compared with the state-of-the-art. We further demonstrate how our delighting method can enhance the performance of light-sensitive computer vision tasks such as face relighting and semantic parsing, allowing them to handle extreme lighting conditions.  ( 2 min )

Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches. Classically, image registration is performed by optimizing a complex objective similarity function, leading to long run times. This contributes to a convention for aligning all data to a global average reference frame that poorly reflects the underlying cortical heterogeneity. In this paper, we propose a novel unsupervised learning-based framework that converts registration to a multi-label classification problem, where each point in a low-resolution control grid deforms to one of fixed, finite number of endpoints. This is learned using a spherical geometric deep learning architecture, in an end-to-end unsupervised way, with regularization imposed using a deep Conditional Random Field (CRF). Experiments show that our proposed framework performs competitively, in terms of similarity and areal distortion, relative to the most popular classical surface registration algorithms and generates smoother deformations than other learning-based surface registration methods, even in subjects with atypical cortical morphology.  ( 2 min )

Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographic (sEMG) signals is one of the most promising approaches, given that sEMG signal acquisition is non-invasive and is directly related to muscle contraction. However, the analysis of these signals still presents many challenges since similar gestures result in similar muscle contractions. Thus the resulting signal shapes are almost identical, leading to low classification accuracy. To tackle this challenge, complex neural networks are employed, which require large memory footprints, consume relatively high energy and limit the maximum battery life of devices used for classification. This work addresses this problem with the introduction of the Bioformers. This new family of ultra-small attention-based architectures approaches state-of-the-art performance while reducing the number of parameters and operations of 4.9X. Additionally, by introducing a new inter-subjects pre-training, we improve the accuracy of our best Bioformer by 3.39%, matching state-of-the-art accuracy without any additional inference cost. Deploying our best performing Bioformer on a Parallel, Ultra-Low Power (PULP) microcontroller unit (MCU), the GreenWaves GAP8, we achieve an inference latency and energy of 2.72 ms and 0.14 mJ, respectively, 8.0X lower than the previous state-of-the-art neural network, while occupying just 94.2 kB of memory.  ( 2 min )

Recent localization frameworks exploit spatial information of complex channel measurements (CMs) to estimate accurate positions even in multipath propagation scenarios. State-of-the art CM fingerprinting(FP)-based methods employ convolutional neural networks (CNN) to extract the spatial information. However, they need spatially dense data sets (associated with high acquisition and maintenance efforts) to work well -- which is rarely the case in practical applications. If such data is not available (or its quality is low), we cannot compensate the performance degradation of CNN-based FP as they do not provide statistical position estimates, which prevents a fusion with other sources of information on the observation level. We propose a novel localization framework that adapts well to sparse datasets that only contain CMs of specific areas within the environment with strong multipath propagation. Our framework compresses CMs into informative features to unravel spatial information. It then regresses Gaussian processes (GPs) for each of them, which imply statistical observation models based on distance-dependent covariance kernels. Our framework combines the trained GPs with line-of-sight ranges and a dynamics model in a particle filter. Our measurements show that our approach outperforms state-of-the-art CNN fingerprinting (0.52 m vs. 1.3 m MAE) on spatially sparse data collected in a realistic industrial indoor environment.  ( 2 min )

Recently, fine-tuning language models pre-trained on large text corpora have provided huge improvements on vision-and-language (V&L) tasks as well as on pure language tasks. However, fine-tuning the entire parameter set of pre-trained models becomes impractical since the model size is growing rapidly. Hence, in this paper, we introduce adapter-based parameter-efficient transfer learning techniques to V&L models such as VL-BART and VLT5. We evaluate our methods in a unified multi-task setup on both image-text and video-text benchmarks. For the image-text tasks, we use four diverse V&L datasets: VQAv2, GQA, NLVR2 , and MSCOCO image captioning. For video-text tasks, we use TVQA, How2QA, TVC, and YC2C. With careful training and thorough experiments, we benchmark three popular adapter-based methods (Adapter, Hyperformer, Compacter) against the standard full fine-tuning and the recently proposed prompt-tuning approach. We also enhance the efficiency and performance of adapters by sharing their weights to attain knowledge across tasks. Our results demonstrate that training the adapter with the weight-sharing technique (4.18% of total parameters for image-text tasks and 3.39% for video-text tasks) can match the performance of fine-tuning the entire model. Lastly, we present a comprehensive analysis including the combination of adapter and task-specific prompts and the impact of V&L pre-training on adapters. Our code is available at: https://github.com/ylsung/VL_adapter.  ( 2 min )

The goal of most subjective studies is to place a set of stimuli on a perceptual scale. This is mostly done directly by rating, e.g. using single or double stimulus methodologies, or indirectly by ranking or pairwise comparison. All these methods estimate the perceptual magnitudes of the stimuli on a scale. However, procedures such as Maximum Likelihood Difference Scaling (MLDS) have shown that considering perceptual distances can bring benefits in terms of discriminatory power, observers' cognitive load, and the number of trials required. One of the disadvantages of the MLDS method is that the perceptual scales obtained for stimuli created from different source content are generally not comparable. In this paper, we propose an extension of the MLDS method that ensures inter-content comparability of the results and shows its usefulness especially in the presence of observer errors.  ( 2 min )

Distributed training algorithms of deep neural networks show impressive convergence speedup properties on very large problems. However, they inherently suffer from communication related slowdowns and communication topology becomes a crucial design choice. Common approaches supported by most machine learning frameworks are: 1) Synchronous decentralized algorithms relying on a peer-to-peer All Reduce topology that is sensitive to stragglers and communication delays. 2) Asynchronous centralised algorithms with a server based topology that is prone to communication bottleneck. Researchers also suggested asynchronous decentralized algorithms designed to avoid the bottleneck and speedup training, however, those commonly use inexact sparse averaging that may lead to a degradation in accuracy. In this paper, we propose Local Asynchronous SGD (LASGD), an asynchronous decentralized algorithm that relies on All Reduce for model synchronization. We empirically validate LASGD's performance on image classification tasks on the ImageNet dataset. Our experiments demonstrate that LASGD accelerates training compared to SGD and state of the art gossip based approaches.  ( 2 min )

Novel prediction methods should always be compared to a baseline to know how well they perform. Without this frame of reference, the performance score of a model is basically meaningless. What does it mean when a model achieves an $F_1$ of 0.8 on a test set? A proper baseline is needed to evaluate the `goodness' of a performance score. Comparing with the latest state-of-the-art model is usually insightful. However, being state-of-the-art can change rapidly when newer models are developed. Contrary to an advanced model, a simple dummy classifier could be used. However, the latter could be beaten too easily, making the comparison less valuable. This paper presents a universal baseline method for all binary classification models, named the Dutch Draw (DD). This approach weighs simple classifiers and determines the best classifier to use as a baseline. We theoretically derive the DD baseline for many commonly used evaluation measures and show that in most situations it reduces to (almost) always predicting either zero or one. Summarizing, the DD baseline is: (1) general, as it is applicable to all binary classification problems; (2) simple, as it is quickly determined without training or parameter-tuning; (3) informative, as insightful conclusions can be drawn from the results. The DD baseline serves two purposes. First, to enable comparisons across research papers by this robust and universal baseline. Secondly, to provide a sanity check during the development process of a prediction model. It is a major warning sign when a model is outperformed by the DD baseline.  ( 2 min )

In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) -- while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learner  ( 2 min )

Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining. The intrinsic complexity of these tasks demands powerful learning models. While pretrained Transformer-based Language Models (LM) have been shown to provide state-of-the-art results over different NLP tasks, the scarcity of manually annotated data and the highly domain-dependent nature of argumentation restrict the capabilities of such models. In this work, we propose a novel transfer learning strategy to overcome these challenges. We utilize argumentation-rich social discussions from the ChangeMyView subreddit as a source of unsupervised, argumentative discourse-aware knowledge by finetuning pretrained LMs on a selectively masked language modeling task. Furthermore, we introduce a novel prompt-based strategy for inter-component relation prediction that compliments our proposed finetuning method while leveraging on the discourse context. Exhaustive experiments show the generalization capability of our method on these two tasks over within-domain as well as out-of-domain datasets, outperforming several existing and employed strong baselines.  ( 2 min )

Recent technology development brings the booming of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehousing. In DDS, a service loop is an elemental structure, including its service worker, the service providers and corresponding service targets. The service workers should transport either humans or parcels from the providers to the target locations. Various planning tasks within DDS can thus be classified into two individual stages: 1) Dispatching, which is to form service loops from demand/supply distributions, and 2)Routing, which is to decide specific serving orders within the constructed loops. Generating high-quality strategies in both stages is important to develop DDS but faces several challenging. Meanwhile, deep reinforcement learning (DRL) has been developed rapidly in recent years. It is a powerful tool to solve these problems since DRL can learn a parametric model without relying on too many problem-based assumptions and optimize long-term effect by learning sequential decisions. In this survey, we first define DDS, then highlight common applications and important decision/control problems within. For each problem, we comprehensively introduce the existing DRL solutions. We also introduce open simulation environments for development and evaluation of DDS applications. Finally, we analyze remaining challenges and discuss further research opportunities in DRL solutions for DDS.  ( 2 min )

Simulations of large-scale plasma systems are typically based on a fluid approximation approach. These models construct a moment-based system of equations that approximate the particle-based physics as a fluid, but as a result lack the small-scale physical processes available to fully kinetic models. Traditionally, empirical closure relations are used to close the moment-based system of equations, which typically approximate the pressure tensor or heat flux. The more accurate the closure relation, the stronger the simulation approaches kinetic-based results. In this paper, new closure terms are constructed using machine learning techniques. Two different machine learning models, a multi-layer perceptron and a gradient boosting regressor, synthesize a local closure relation for the pressure tensor and heat flux vector from fully kinetic simulations of a 2D magnetic reconnection problem. The models are compared to an existing closure relation for the pressure tensor, and the applicability of the models is discussed. The initial results show that the models can capture the diagonal components of the pressure tensor accurately, and show promising results for the heat flux, opening the way for new experiments in multi-scale modeling. We find that the sampling of the points used to train both models play a capital role in their accuracy.  ( 2 min )

The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centric approach to AI. Data scientists spend the majority of their time studying and wrangling the data, yet tools to aid them with data analysis are lacking. This study identifies the recurrent data quality issues in public datasets. Analogous to code smells, we introduce a novel catalogue of data smells that can be used to indicate early signs of problems or technical debt in machine learning systems. To understand the prevalence of data quality issues in datasets, we analyse 25 public datasets and identify 14 data smells.  ( 2 min )

Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate such behavior through the lens of geometry of the loss and Hessian eigenspectrum, linking the model's lack of generalization capacity to the sharpness of the solution. Motivated by prior studies connecting the sharpness of the loss surface and the generalization gap, we show that i) training clients locally with Sharpness-Aware Minimization (SAM) or its adaptive version (ASAM) and ii) averaging stochastic weights (SWA) on the server-side can substantially improve generalization in Federated Learning and help bridging the gap with centralized models. By seeking parameters in neighborhoods having uniform low loss, the model converges towards flatter minima and its generalization significantly improves in both homogeneous and heterogeneous scenarios. Empirical results demonstrate the effectiveness of those optimizers across a variety of benchmark vision datasets (e.g. CIFAR10/100, Landmarks-User-160k, IDDA) and tasks (large scale classification, semantic segmentation, domain generalization).  ( 2 min )

The recently proposed CheckList (Riberio et al,. 2020) approach to evaluation of NLP systems has revealed high failure rates for basic capabilities for multiple state-of-the-art and commercial models. However, the CheckList creation process is manual which creates a bottleneck towards creation of multilingual CheckLists catering 100s of languages. In this work, we explore multiple approaches to generate and evaluate the quality of Multilingual CheckList. We device an algorithm -- Automated Multilingual Checklist Generation (AMCG) for automatically transferring a CheckList from a source to a target language that relies on a reasonable machine translation system. We then compare the CheckList generated by AMCG with CheckLists generated with different levels of human intervention. Through in-depth crosslingual experiments between English and Hindi, and broad multilingual experiments spanning 11 languages, we show that the automatic approach can provide accurate estimates of failure rates of a model across capabilities, as would a human-verified CheckList, and better than CheckLists generated by humans from scratch.  ( 2 min )

In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. During this attack, the original data batch is reconstructed given model weights and the corresponding gradients. We introduce a method, named GradViT, that optimizes random noise into naturally looking images via an iterative process. The optimization objective consists of (i) a loss on matching the gradients, (ii) image prior in the form of distance to batch-normalization statistics of a pretrained CNN model, and (iii) a total variation regularization on patches to guide correct recovery locations. We propose a unique loss scheduling function to overcome local minima during optimization. We evaluate GadViT on ImageNet1K and MS-Celeb-1M datasets, and observe unprecedentedly high fidelity and closeness to the original (hidden) data. During the analysis we find that vision transformers are significantly more vulnerable than previously studied CNNs due to the presence of the attention mechanism. Our method demonstrates new state-of-the-art results for gradient inversion in both qualitative and quantitative metrics. Project page at https://gradvit.github.io/.  ( 2 min )

A difficult step in the process of karyotyping is segmenting chromosomes that touch or overlap. In an attempt to automate the process, previous studies turned to Deep Learning methods, with some formulating the task as a semantic segmentation problem. These models treat separate chromosome instances as semantic classes, which we show to be problematic, since it is uncertain which chromosome should be classed as #1 and #2. Assigning class labels based on comparison rules, such as the shorter/longer chromosome alleviates, but does not fully resolve the issue. Instead, we separate the chromosome instances in a second stage, predicting the orientation of the chromosomes by the model and use it as one of the key distinguishing factors of the chromosomes. We demonstrate this method to be effective. Furthermore, we introduce a novel Double-Angle representation that a neural network can use to predict the orientation. The representation maps any direction and its reverse to the same point. Lastly, we present a new expanded synthetic dataset, which is based on Pommier's dataset, but addresses its issues with insufficient separation between its training and testing sets.  ( 2 min )

Named entity recognition (NER) is the process of recognising and classifying important information (entities) in text. Proper nouns, such as a person's name, an organization's name, or a location's name, are examples of entities. The NER is one of the important modules in applications like human resources, customer support, search engines, content classification, and academia. In this work, we consider NER for low-resource Indian languages like Hindi and Marathi. The transformer-based models have been widely used for NER tasks. We consider different variations of BERT like base-BERT, RoBERTa, and AlBERT and benchmark them on publicly available Hindi and Marathi NER datasets. We provide an exhaustive comparison of different monolingual and multilingual transformer-based models and establish simple baselines currently missing in the literature. We show that the monolingual MahaRoBERTa model performs the best for Marathi NER whereas the multilingual XLM-RoBERTa performs the best for Hindi NER. We also perform cross-language evaluation and present mixed observations.  ( 2 min )

Convolutional neural networks (CNN) have demonstrated outstanding Compressed Sensing (CS) performance compared to traditional, hand-crafted methods. However, they are broadly limited in terms of generalisability, inductive bias and difficulty to model long distance relationships. Transformer neural networks (TNN) overcome such issues by implementing an attention mechanism designed to capture dependencies between inputs. However, high-resolution tasks typically require vision Transformers (ViT) to decompose an image into patch-based tokens, limiting inputs to inherently local contexts. We propose a novel image decomposition that naturally embeds images into low-resolution inputs. These Kaleidoscope tokens (KD) provide a mechanism for global attention, at the same computational cost as a patch-based approach. To showcase this development, we replace CNN components in a well-known CS-MRI neural network with TNN blocks and demonstrate the improvements afforded by KD. We also propose an ensemble of image tokens, which enhance overall image quality and reduces model size. Supplementary material is available: https://github.com/uqmarlonbran/TCS.git}{https://github.com/uqmarlonbran/TCS.git  ( 2 min )

We investigate both stationary and time-varying, nonmonotone generalized Nash equilibrium problems that exhibit symmetric interactions among the agents, which are known to be potential. As may happen in practical cases, however, we envision a scenario in which the formal expression of the underlying potential function is not available, and we design a semi-decentralized Nash equilibrium seeking algorithm. In the proposed two-layer scheme, a coordinator iteratively integrates the (possibly noisy and sporadic) agents' feedback to learn the pseudo-gradients of the agents, and then design personalized incentives for them. On their side, the agents receive those personalized incentives, compute a solution to an extended game, and then return feedback measurements to the coordinator. In the stationary setting, our algorithm returns a Nash equilibrium in case the coordinator is endowed with standard learning policies, while it returns a Nash equilibrium up to a constant, yet adjustable, error in the time-varying case. As a motivating application, we consider the ridehailing service provided by several companies with mobility as a service orchestration, necessary to both handle competition among firms and avoid traffic congestion, which is also adopted to run numerical experiments verifying our results.  ( 2 min )

We develop and analyze algorithms for distributionally robust optimization (DRO) of convex losses. In particular, we consider group-structured and bounded $f$-divergence uncertainty sets. Our approach relies on an accelerated method that queries a ball optimization oracle, i.e., a subroutine that minimizes the objective within a small ball around the query point. Our main contribution is efficient implementations of this oracle for DRO objectives. For DRO with $N$ non-smooth loss functions, the resulting algorithms find an $\epsilon$-accurate solution with $\widetilde{O}\left(N\epsilon^{-2/3} + \epsilon^{-2}\right)$ first-order oracle queries to individual loss functions. Compared to existing algorithms for this problem, we improve complexity by a factor of up to $\epsilon^{-4/3}$.  ( 2 min )

Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for future lepton colliders such as the International Linear Collider. We deploy two networks; one is simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder-decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC reconstruction algorithm.

Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have only been made on the problem within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, particularly those having several continuous-valued features. Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. We show that by using these guesses, we can reduce the run time by multiple orders of magnitude, while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power. Our approach enables guesses about how to bin continuous features, the size of the tree, and lower bounds on the error for the optimal decision tree. Our experiments show that in many cases we can rapidly construct sparse decision trees that match the accuracy of black box models. To summarize: when you are having trouble optimizing, just guess.  ( 2 min )

Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains. While these methods have incrementally improved the generated image fidelity and text relevancy, several pivotal gaps remain unanswered, limiting applicability and quality. We propose a novel text-to-image method that addresses these gaps by (i) enabling a simple control mechanism complementary to text in the form of a scene, (ii) introducing elements that substantially improve the tokenization process by employing domain-specific knowledge over key image regions (faces and salient objects), and (iii) adapting classifier-free guidance for the transformer use case. Our model achieves state-of-the-art FID and human evaluation results, unlocking the ability to generate high fidelity images in a resolution of 512x512 pixels, significantly improving visual quality. Through scene controllability, we introduce several new capabilities: (i) Scene editing, (ii) text editing with anchor scenes, (iii) overcoming out-of-distribution text prompts, and (iv) story illustration generation, as demonstrated in the story we wrote.

Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the convergence rate of the FL process. Active client selection strategies are popularly proposed in recent studies. However, they neglect the loss correlations between the clients and achieve only marginal improvement compared to the uniform selection strategy. In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). Based on the GP model, we derive a client selection strategy with a significant reduction of expected global loss in each round. Besides, we develop an efficient GP training method with a low communication overhead in the FL scenario by utilizing the covariance stationarity. Our experimental results show that compared to the state-of-the-art method, FedCorr can improve the convergence rates by $34\%\sim 99\%$ and $26\%\sim 51\%$ on FMNIST and CIFAR-10, respectively.

We study the implicit bias of gradient based training methods to favor low-depth solutions when training deep neural networks. Recent results in the literature suggest that penultimate layer representations learned by a classifier over multiple classes exhibit a clustering property, called neural collapse. We demonstrate empirically that the neural collapse property extends beyond the penultimate layer and tends to emerge in intermediate layers as well. In this regards, we hypothesize that gradient based methods are implicitly biased towards selecting neural networks of minimal depth for achieving this clustering property.

Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation for circuits, which preserves netlist data during the whole learning process, and enables the congestion information propagated geometrically and topologically. Based on the formulation, we further developed a heterogeneous graph neural network architecture LHNN, jointing the routing demand regression to support the congestion spot classification. LHNN constantly achieves more than 35% improvements compared with U-nets and Pix2Pix on the F1 score. We expect our work shall highlight essential procedures using machine learning for congestion prediction.

Background and Objective: Advances in electron microscopy (EM) now allow three-dimensional (3D) imaging of hundreds of micrometers of tissue with nanometer-scale resolution, providing new opportunities to study the ultrastructure of the brain. In this work, we introduce a freely available Matlab-based gACSON software for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes of brain tissue samples. Methods: The software is equipped with a graphical user interface (GUI). It automatically segments the intra-axonal space of myelinated axons and their corresponding myelin sheaths and allows manual segmentation, proofreading, and interactive correction of the segmented components. gACSON analyzes the morphology of myelinated axons, such as axonal diameter, axonal eccentricity, myelin thickness, or g-ratio. Results: We illustrate the use of the software by segmenting and analyzing myelinated axons in six 3D-EM volumes of rat somatosensory cortex after sham surgery or traumatic brain injury (TBI). Our results suggest that the equivalent diameter of myelinated axons in somatosensory cortex was decreased in TBI animals five months after the injury. Conclusions: Our results indicate that gACSON is a valuable tool for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes. It is freely available at https://github.com/AndreaBehan/g-ACSON under the MIT license.

This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth's Mover Distance (EMD) between spike trains with spiking neural networks (SNN). We train this model on images of the MNIST dataset converted into spiking domain with novel conversion schemes. The quality of the siamese embeddings of input images was evaluated by measuring the classifier performance for different dataset coding types. The models achieved performance similar to existing SNN-based approaches (F1-score of up to 0.9386) while using only about 15% of hidden layer neurons to classify each example. Furthermore, models which did not employ a sparse neural code were about 45% slower than their sparse counterparts. These properties make the model suitable for low energy consumption and low prediction latency applications.

We have developed a technique combining the accuracy of quantum Monte Carlo in describing the electron correlation with the efficiency of a Machine Learning Potential (MLP). We use kernel regression in combination with SOAP (Smooth Overlap of Atomic Position) features, implemented here in a very efficient way. The key ingredients are: i) a sparsification technique, based on farthest point sampling, ensuring generality and transferability of our MLPs and ii) the so called $\Delta$-learning, allowing a small training data set, a fundamental property for highly accurate but computationally demanding calculations, such as the ones based on quantum Monte Carlo. As the first application we present a benchmark study of the liquid-liquid transition of high-pressure hydrogen and show the quality of our MLP, by emphasizing the importance of high accuracy for this very debated subject, where experiments are difficult in the lab, and theory is still far from being conclusive.

When trained on diverse labeled data, machine learning models have proven themselves to be a powerful tool in all facets of society. However, due to budget limitations, deliberate or non-deliberate censorship, and other problems during data collection and curation, the labeled training set might exhibit a systematic shortage of data for certain groups. We investigate a scenario in which the absence of certain data is linked to the second level of a two-level hierarchy in the data. Inspired by the idea of protected groups from algorithmic fairness, we refer to the partitions carved by this second level as "subgroups"; we refer to combinations of subgroups and classes, or leaves of the hierarchy, as "sources". To characterize the problem, we introduce the concept of classes with incomplete subgroup support. The representational bias in the training set can give rise to spurious correlations between the classes and the subgroups which render standard classification models ungeneralizable to unseen sources. To overcome this bias, we make use of an additional, diverse but unlabeled dataset, called the "deployment set", to learn a representation that is invariant to subgroup. This is done by adversarially matching the support of the training and deployment sets in representation space. In order to learn the desired invariance, it is paramount that the sets of samples observed by the discriminator are balanced by class; this is easily achieved for the training set, but requires using semi-supervised clustering for the deployment set. We demonstrate the effectiveness of our method with experiments on several datasets and variants of the problem.

An activation function is a crucial component of a neural network that introduces non-linearity in the network. The state-of-the-art performance of a neural network depends also on the perfect choice of an activation function. We propose two novel non-monotonic smooth trainable activation functions, called ErfAct and Pserf. Experiments suggest that the proposed functions improve the network performance significantly compared to the widely used activations like ReLU, Swish, and Mish. Replacing ReLU by ErfAct and Pserf, we have 5.68% and 5.42% improvement for top-1 accuracy on Shufflenet V2 (2.0x) network in CIFAR100 dataset, 2.11% and 1.96% improvement for top-1 accuracy on Shufflenet V2 (2.0x) network in CIFAR10 dataset, 1.0%, and 1.0% improvement on mean average precision (mAP) on SSD300 model in Pascal VOC dataset.  ( 2 min )

We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. By using the quantum computer as a processor for probability distributions, a probabilistic traversal of the decision tree can be realized via measurements of a quantum circuit. We describe how tree inductions and the prediction of class labels of query data can be integrated into this framework. An on-demand sampling method enables predictions with a constant number of classical memory slots, independent of the tree depth. We experimentally study our approach using both a quantum computing simulator and actual IBM quantum hardware. To our knowledge, this is the first realization of a decision tree classifier on a quantum device.

Transformer-based language models (TLMs) provide state-of-the-art performance in many modern natural language processing applications. TLM training is conducted in two phases. First, the model is pre-trained over large volumes of text to minimize a generic objective function, such as the Masked Language Model (MLM). Second, the model is fine-tuned in specific downstream tasks. Pre-training requires large volumes of data and high computational resources, while introducing many still unresolved design choices. For instance, selecting hyperparameters for language model pre-training is often carried out based on heuristics or grid-based searches. In this work, we propose a multi-armed bandit-based online optimization framework for the sequential selection of pre-training hyperparameters to optimize language model performance. We pose the pre-training procedure as a sequential decision-making task, where at each pre-training step, an agent must determine what hyperparameters to use towards optimizing the pre-training objective. We propose a Thompson sampling bandit algorithm, based on a surrogate Gaussian process reward model of the MLM pre-training objective, for its sequential minimization. We empirically show how the proposed Gaussian process based Thompson sampling pre-trains robust and well-performing language models. Namely, by sequentially selecting masking hyperparameters of the TLM, we achieve satisfactory performance in less epochs, not only in terms of the pre-training MLM objective, but in diverse downstream fine-tuning tasks. The proposed bandit-based technique provides an automated hyperparameter selection method for pre-training TLMs of interest to practitioners. In addition, our results indicate that, instead of MLM pre-training with fixed masking probabilities, sequentially adapting the masking hyperparameters improves both pre-training loss and downstream task metrics.

In real-world applications, it is important and desirable to learn a model that performs well on out-of-distribution (OOD) data. Recently, causality has become a powerful tool to tackle the OOD generalization problem, with the idea resting on the causal mechanism that is invariant across domains of interest. To leverage the generally unknown causal mechanism, existing works assume a linear form of causal feature or require sufficiently many and diverse training domains, which are usually restrictive in practice. In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature. Our approach is based on transformations that modify the non-causal feature but leave the causal part unchanged, which can be either obtained from prior knowledge or learned from the training data in the multi-domain scenario. Under the setting of invariant causal mechanism, we theoretically show that if all such transformations are available, then we can learn a minimax optimal model across the domains using only single domain data. Noticing that knowing a complete set of these causal invariant transformations may be impractical, we further show that it suffices to know only a subset of these transformations. Based on the theoretical findings, a regularized training procedure is proposed to improve the OOD generalization capability. Extensive experimental results on both synthetic and real datasets verify the effectiveness of the proposed algorithm, even with only a few causal invariant transformations.

Variational representations of divergences and distances between high-dimensional probability distributions offer significant theoretical insights and practical advantages in numerous research areas. Recently, they have gained popularity in machine learning as a tractable and scalable approach for training probabilistic models and for statistically differentiating between data distributions. Their advantages include: 1) They can be estimated from data as statistical averages. 2) Such representations can leverage the ability of neural networks to efficiently approximate optimal solutions in function spaces. However, a systematic and practical approach to improving the tightness of such variational formulas, and accordingly accelerate statistical learning and estimation from data, is currently lacking. Here we develop such a methodology for building new, tighter variational representations of divergences. Our approach relies on improved objective functionals constructed via an auxiliary optimization problem. Furthermore, the calculation of the functional Hessian of objective functionals unveils the local curvature differences around the common optimal variational solution; this quantifies and orders the tightness gains between different variational representations. Finally, numerical simulations utilizing neural network optimization demonstrate that tighter representations can result in significantly faster learning and more accurate estimation of divergences in both synthetic and real datasets (of more than 1000 dimensions), often accelerated by nearly an order of magnitude.

In this work, we study the computational complexity of determining whether a machine learning model that perfectly fits the training data will generalizes to unseen data. In particular, we study the power of a malicious agent whose goal is to construct a model g that fits its training data and nothing else, but is indistinguishable from an accurate model f. We say that g strongly spoofs f if no polynomial-time algorithm can tell them apart. If instead we restrict to algorithms that run in $n^c$ time for some fixed $c$, we say that g c-weakly spoofs f. Our main results are 1. Under cryptographic assumptions, strong spoofing is possible and 2. For any c> 0, c-weak spoofing is possible unconditionally While the assumption of a malicious agent is an extreme scenario (hopefully companies training large models are not malicious), we believe that it sheds light on the inherent difficulties of blindly trusting large proprietary models or data.

Optimizing behaviors for dexterous manipulation has been a longstanding challenge in robotics, with a variety of methods from model-based control to model-free reinforcement learning having been previously explored in literature. Perhaps one of the most powerful techniques to learn complex manipulation strategies is imitation learning. However, collecting and learning from demonstrations in dexterous manipulation is quite challenging. The complex, high-dimensional action-space involved with multi-finger control often leads to poor sample efficiency of learning-based methods. In this work, we propose 'Dexterous Imitation Made Easy' (DIME) a new imitation learning framework for dexterous manipulation. DIME only requires a single RGB camera to observe a human operator and teleoperate our robotic hand. Once demonstrations are collected, DIME employs standard imitation learning methods to train dexterous manipulation policies. On both simulation and real robot benchmarks we demonstrate that DIME can be used to solve complex, in-hand manipulation tasks such as 'flipping', 'spinning', and 'rotating' objects with the Allegro hand. Our framework along with pre-collected demonstrations is publicly available at https://nyu-robot-learning.github.io/dime.

De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data. Existing approaches for de novo analysis enumerate MS evidence for all amino acid classes during inference. It leads to over-trimming on receptive fields of MS data and restricts MS evidence associated with following undecoded amino acids. Our approach, DPST, circumvents these limitations with two key components: (1) A confidence value aggregation encoder to sketch spectrum representations according to amino-acid-based connectivity among MS; (2) A global-local fusion decoder to progressively assimilate contextualized spectrum representations with a predefined preconception of localized MS evidence and amino acid priors. Our components originate from a closed-form solution and selectively attend to informative amino-acid-aware MS representations. Through extensive empirical studies, we demonstrate the superiority of DPST, showing that it outperforms state-of-the-art approaches by a margin of 12% - 19% peptide accuracy.

The convolutional neural network (CNN) has been widely applied to process the industrial data based tensor input, which integrates data records of distributed industrial systems from the spatial, temporal, and system dynamics aspects. However, unlike images, information in the industrial data based tensor is not necessarily spatially ordered. Thus, directly applying CNN is ineffective. To tackle such issue, we propose a plug and play module, the Rubik's Cube Operator (RCO), to adaptively permutate the data organization of the industrial data based tensor to an optimal or suboptimal order of attributes before being processed by CNNs, which can be updated with subsequent CNNs together via the gradient-based optimizer. The proposed RCO maintains K binary and right stochastic permutation matrices to permutate attributes of K axes of the input industrial data based tensor. A novel learning process is proposed to enable learning permutation matrices from data, where the Gumbel-Softmax is employed to reparameterize elements of permutation matrices, and the soft regularization loss is proposed and added to the task-specific loss to ensure the feature diversity of the permuted data. We verify the effectiveness of the proposed RCO via considering two representative learning tasks processing industrial data via CNNs, the wind power prediction (WPP) and the wind speed prediction (WSP) from the renewable energy domain. Computational experiments are conducted based on four datasets collected from different wind farms and the results demonstrate that the proposed RCO can improve the performance of CNN based networks significantly.

In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in non-stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a metric. Using a non-parametric model of the MDP built with time-dependent kernels, we prove a regret bound that scales with the covering dimension of the state-action space and the total variation of the MDP with time, which quantifies its level of non-stationarity. Our method generalizes previous approaches based on sliding windows and exponential discounting used to handle changing environments. We further propose a practical implementation of KeRNS, we analyze its regret and validate it experimentally.

This paper gives the first polynomial-time algorithm for tabular Markov Decision Processes (MDP) that enjoys a regret bound \emph{independent on the planning horizon}. Specifically, we consider tabular MDP with $S$ states, $A$ actions, a planning horizon $H$, total reward bounded by $1$, and the agent plays for $K$ episodes. We design an algorithm that achieves an $O\left(\mathrm{poly}(S,A,\log K)\sqrt{K}\right)$ regret in contrast to existing bounds which either has an additional $\mathrm{polylog}(H)$ dependency~\citep{zhang2020reinforcement} or has an exponential dependency on $S$~\citep{li2021settling}. Our result relies on a sequence of new structural lemmas establishing the approximation power, stability, and concentration property of stationary policies, which can have applications in other problems related to Markov chains.

One of the major challenges in training text-to-image generation models is the need of a large number of high-quality image-text pairs. While image samples are often easily accessible, the associated text descriptions typically require careful human captioning, which is particularly time- and cost-consuming. In this paper, we propose the first work to train text-to-image generation models without any text data. Our method leverages the well-aligned multi-modal semantic space of the powerful pre-trained CLIP model: the requirement of text-conditioning is seamlessly alleviated via generating text features from image features. Extensive experiments are conducted to illustrate the effectiveness of the proposed method. We obtain state-of-the-art results in the standard text-to-image generation tasks. Importantly, the proposed language-free model outperforms most existing models trained with full image-text pairs. Furthermore, our method can be applied in fine-tuning pre-trained models, which saves both training time and cost in training text-to-image generation models. Our pre-trained model obtains competitive results in zero-shot text-to-image generation on the MS-COCO dataset, yet with around only 1% of the model size and training data size relative to the recently proposed large DALL-E model.

Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch and supports any OpenAI Gym environment. Its design is based on Avalanche, one of the more popular continual learning libraries, which allow us to reuse a large number of continual learning strategies and improve the interaction between reinforcement learning and continual learning researchers. Additionally, we propose Continual Habitat-Lab, a novel benchmark and a high-level library which enables the usage of the photorealistic simulator Habitat-Sim for CRL research. Overall, Avalanche RL attempts to unify under a common framework continual reinforcement learning applications, which we hope will foster the growth of the field.

Entity resolution (ER) aims at matching records that refer to the same real-world entity. Although widely studied for the last 50 years, ER still represents a challenging data management problem, and several recent works have started to investigate the opportunity of applying deep learning (DL) techniques to solve this problem. In this paper, we study the fundamental problem of explainability of the DL solution for ER. Understanding the matching predictions of an ER solution is indeed crucial to assess the trustworthiness of the DL model and to discover its biases. We treat the DL model as a black box classifier and - while previous approaches to provide explanations for DL predictions are agnostic to the classification task. we propose the CERTA approach that is aware of the semantics of the ER problem. Our approach produces both saliency explanations, which associate each attribute with a saliency score, and counterfactual explanations, which provide examples of values that can flip the prediction. CERTA builds on a probabilistic framework that aims at computing the explanations evaluating the outcomes produced by using perturbed copies of the input records. We experimentally evaluate CERTA's explanations of state-of-the-art ER solutions based on DL models using publicly available datasets, and demonstrate the effectiveness of CERTA over recently proposed methods for this problem.

Synthesis from linear temporal logic (LTL) specifications provides assured controllers for autonomous systems operating in stochastic and potentially adversarial environments. Automatic synthesis tools, however, require a model of the environment to construct controllers. In this work, we introduce a model-free reinforcement learning (RL) approach that derives controllers from given LTL specifications even when the environment is completely unknown. We model the problem of satisfying the LTL specifications as a stochastic game (SG) between the controller and the adversarial environment; we then learn optimal controller strategies that maximize the probability of satisfying the LTL specifications against the worst-case environment behavior. We first construct a product game using the deterministic parity automaton (DPA) translated from the given LTL specification. By deriving distinct rewards and discount factors from the acceptance condition of the DPA, we reduce the maximization of the worst-case probability of satisfying the LTL specification into the maximization of a discounted reward objective in the product game; this allows for the use of model-free RL algorithms to learn an optimal controller strategy. To deal with the common scalability problems when the number of colors defining the acceptance condition of the DPA is large, we propose a lazy color generation method where distinct rewards and discount factors are utilized only when needed, and an approximate method where the controller eventually focuses on only one color. In several case studies, we show that our approach is scalable to a wide range of LTL formulas, significantly outperforming existing methods for learning controllers from LTL specifications in SGs.

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N\in\mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on their local data, in a privacy-preserving manner. SwiftAgg+ can significantly reduce the communication overheads without any compromise on security, and achieve the optimum communication load within a diminishing gap. Specifically, in presence of at most $D$ dropout users, SwiftAgg+ achieves average per-user communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ and the server communication load of $(1+\mathcal{O}(\frac{1}{N}))L$, with a worst-case information-theoretic security guarantee, against any subset of up to $T$ semi-honest users who may also collude with the curious server. The proposed SwiftAgg+ has also a flexibility to reduce the number of active communication links at the cost of increasing the the communication load between the users and the server. In particular, for any $K\in\mathbb{N}$, SwiftAgg+ can achieve the uplink communication load of $(1+\frac{T}{K})L$, and per-user communication load of up to $(1-\frac{1}{N})(1+\frac{T+D}{K})L$, where the number of pair-wise active connections in the network is $\frac{N}{2}(K+T+D+1)$.

In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent evaluation of AI models stratified across race sub-populations have revealed inequalities in how patients are diagnosed, given treatments, and billed for healthcare costs. In this perspective article, we summarize the intersectional field of fairness in machine learning through the context of current issues in healthcare, outline how algorithmic biases (e.g. - image acquisition, genetic variation, intra-observer labeling variability) arise in current clinical workflows and their resulting healthcare disparities. Lastly, we also review emerging technology for mitigating bias via federated learning, disentanglement, and model explainability, and their role in AI-SaMD development.

With the vigorous development of multimedia equipment and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has drawn lots of attention in recent years, there still remain some problems. The first point is that existing methods are mainly designed in batch mode and not able to efficiently handle streaming multi-modal data. The second point is that all existing online multi-modal hashing methods fail to effectively handle unseen new classes which come continuously with streaming data chunks. In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). We design novel semantic-enhanced representation for data, which could help handle the new coming classes, and thereby construct the enhanced semantic objective function. An efficient and effective discrete online optimization algorithm is further proposed for OASIS. Extensive experiments show that our method can exceed the state-of-the-art models. For good reproducibility and benefiting the community, our code and data are already available in supplementary material and will be made publicly available.

Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis. We introduce an automated exploration approach and a library of optimized kernels to map TCNs on Parallel Ultra-Low Power (PULP) microcontrollers. Our approach minimizes latency and energy by exploiting a layer tiling optimizer to jointly find the tiling dimensions and select among alternative implementations of the causal and dilated 1D-convolution operations at the core of TCNs. We benchmark our approach on a commercial PULP device, achieving up to 103X lower latency and 20.3X lower energy than the Cube-AI toolkit executed on the STM32L4 and from 2.9X to 26.6X lower energy compared to commercial closed-source and academic open-source approaches on the same hardware target.

We propose sanitizer, a framework for secure and task-agnostic data release. While releasing datasets continues to make a big impact in various applications of computer vision, its impact is mostly realized when data sharing is not inhibited by privacy concerns. We alleviate these concerns by sanitizing datasets in a two-stage process. First, we introduce a global decoupling stage for decomposing raw data into sensitive and non-sensitive latent representations. Secondly, we design a local sampling stage to synthetically generate sensitive information with differential privacy and merge it with non-sensitive latent features to create a useful representation while preserving the privacy. This newly formed latent information is a task-agnostic representation of the original dataset with anonymized sensitive information. While most algorithms sanitize data in a task-dependent manner, a few task-agnostic sanitization techniques sanitize data by censoring sensitive information. In this work, we show that a better privacy-utility trade-off is achieved if sensitive information can be synthesized privately. We validate the effectiveness of the sanitizer by outperforming state-of-the-art baselines on the existing benchmark tasks and demonstrating tasks that are not possible using existing techniques.

The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort to iterative search methods on the training set, which consume hundreds or even thousands of GPU-hours. In this study, we reveal that some unique learnable parameters in quantization, namely the scale factors in the quantizer, can serve as importance indicators of a layer, reflecting the contribution of that layer to the final accuracy at certain bit-widths. These importance indicators naturally perceive the numerical transformation during quantization-aware training, which can precisely and correctly provide quantization sensitivity metrics of layers. However, a deep network always contains hundreds of such indicators, and training them one by one would lead to an excessive time cost. To overcome this issue, we propose a joint training scheme that can obtain all indicators at once. It considerably speeds up the indicators training process by parallelizing the original sequential training processes. With these learned importance indicators, we formulate the MPQ search problem as a one-time integer linear programming (ILP) problem. That avoids the iterative search and significantly reduces search time without limiting the bit-width search space. For example, MPQ search on ResNet18 with our indicators takes only 0.06 seconds. Also, extensive experiments show our approach can achieve SOTA accuracy on ImageNet for far-ranging models with various constraints (e.g., BitOps, compress rate).

Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control.

When using recurrent neural networks (RNNs) it is common practice to apply trained models to sequences longer than those seen in training. This "extrapolating" usage deviates from the traditional statistical learning setup where guarantees are provided under the assumption that train and test distributions are identical. Here we set out to understand when RNNs can extrapolate, focusing on a simple case where the data generating distribution is memoryless. We first show that even with infinite training data, there exist RNN models that interpolate perfectly (i.e., they fit the training data) yet extrapolate poorly to longer sequences. We then show that if gradient descent is used for training, learning will converge to perfect extrapolation under certain assumptions on initialization. Our results complement recent studies on the implicit bias of gradient descent, showing that it plays a key role in extrapolation when learning temporal prediction models.

Kullback-Leibler (KL) control enables efficient numerical methods for nonlinear optimal control problems. The crucial assumption of KL control is the full controllability of the transition distribution. However, this assumption is often violated when the dynamics evolves in a continuous space. Consequently, applying KL control to problems with continuous spaces requires some approximation, which leads to the lost of the optimality. To avoid such approximation, in this paper, we reformulate the KL control problem for continuous spaces so that it does not require unrealistic assumptions. The key difference between the original and reformulated KL control is that the former measures the control effort by KL divergence between controlled and uncontrolled transition distributions while the latter replaces the uncontrolled transition by a noise-driven transition. We show that the reformulated KL control admits efficient numerical algorithms like the original one without unreasonable assumptions. Specifically, the associated value function can be computed by using a Monte Carlo method based on its path integral representation.

We apply deep metric learning for the first time to the problem of classifying planktic foraminifer shells on microscopic images. This species recognition task is an important information source and scientific pillar for reconstructing past climates. All foraminifer CNN recognition pipelines in the literature produce black-box classifiers that lack visualization options for human experts and cannot be applied to open-set problems. Here, we benchmark metric learning against these pipelines, produce the first scientific visualization of the phenotypic planktic foraminifer morphology space, and demonstrate that metric learning can be used to cluster species unseen during training. We show that metric learning outperforms all published CNN-based state-of-the-art benchmarks in this domain. We evaluate our approach on the 34,640 expert-annotated images of the Endless Forams public library of 35 modern planktic foraminifera species. Our results on this data show leading 92% accuracy (at 0.84 F1-score) in reproducing expert labels on withheld test data, and 66.5% accuracy (at 0.70 F1-score) when clustering species never encountered in training. We conclude that metric learning is highly effective for this domain and serves as an important tool towards expert-in-the-loop automation of microfossil identification. Keycode, network weights, and data splits are published with this paper for full reproducibility.

Deep neural networks (DNNs) have been successfully applied to many real-world problems, but a complete understanding of their dynamical and computational principles is still lacking. Conventional theoretical frameworks for analysing DNNs often assume random networks with coupling weights obeying Gaussian statistics. However, non-Gaussian, heavy-tailed coupling is a ubiquitous phenomenon in DNNs. Here, by weaving together theories of heavy-tailed random matrices and non-equilibrium statistical physics, we develop a new type of mean field theory for DNNs which predicts that heavy-tailed weights enable the emergence of an extended critical regime without fine-tuning parameters. In this extended critical regime, DNNs exhibit rich and complex propagation dynamics across layers. We further elucidate that the extended criticality endows DNNs with profound computational advantages: balancing the contraction as well as expansion of internal neural representations and speeding up training processes, hence providing a theoretical guide for the design of efficient neural architectures.

Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we show that this success can be extended to other tasks of conditional audio generation. In particular, building upon HiFi vocoders, we propose a novel HiFi++ general framework for neural vocoding, bandwidth extension, and speech enhancement. We show that with the improved generator architecture and simplified multi-discriminator training, HiFi++ performs on par with the state-of-the-art in these tasks while spending significantly less memory and computational resources. The effectiveness of our approach is validated through a series of extensive experiments.

Transformer-based models generally allocate the same amount of computation for each token in a given sequence. We develop a simple but effective "token dropping" method to accelerate the pretraining of transformer models, such as BERT, without degrading its performance on downstream tasks. In short, we drop unimportant tokens starting from an intermediate layer in the model to make the model focus on important tokens; the dropped tokens are later picked up by the last layer of the model so that the model still produces full-length sequences. We leverage the already built-in masked language modeling (MLM) loss to identify unimportant tokens with practically no computational overhead. In our experiments, this simple approach reduces the pretraining cost of BERT by 25% while achieving similar overall fine-tuning performance on standard downstream tasks.

Federated learning is a prime candidate for distributed machine learning at the network edge due to the low communication complexity and privacy protection among other attractive properties. However, existing algorithms face issues with slow convergence and/or robustness of performance due to the considerable heterogeneity of data distribution, computation and communication capability at the edge. In this work, we tackle both of these issues by focusing on the key component of model aggregation in federated learning systems and studying optimal algorithms to perform this task. Particularly, we propose a contextual aggregation scheme that achieves the optimal context-dependent bound on loss reduction in each round of optimization. The aforementioned context-dependent bound is derived from the particular participating devices in that round and an assumption on smoothness of the overall loss function. We show that this aggregation leads to a definite reduction of loss function at every round. Furthermore, we can integrate our aggregation with many existing algorithms to obtain the contextual versions. Our experimental results demonstrate significant improvements in convergence speed and robustness of the contextual versions compared to the original algorithms. We also consider different variants of the contextual aggregation and show robust performance even in the most extreme settings.

Computational protein-protein interaction (PPI) prediction techniques can contribute greatly in reducing time, cost and false-positive interactions compared to experimental approaches. Sequence is one of the key and primary information of proteins that plays a crucial role in PPI prediction. Several machine learning approaches have been applied to exploit the characteristics of PPI datasets. However, these datasets greatly influence the performance of predicting models. So, care should be taken on both dataset curation as well as design of predictive models. Here, we have described our submitted solution with the results of the SeqPIP competition whose objective was to develop comprehensive PPI predictive models from sequence information with high-quality bias-free interaction datasets. A training set of 2000 positive and 2000 negative interactions with sequences was given to us. Our method was evaluated with three independent high-quality interaction test datasets and with other competitors solutions.  ( 2 min )

In the past decades, Computed Tomography (CT) has established itself as one of the most important imaging techniques in medicine. Today, the applicability of CT is only limited by the deposited radiation dose, reduction of which manifests in noisy or incomplete measurements. Thus, the need for robust reconstruction algorithms arises. In this work, we learn a parametric regularizer with a global receptive field by maximizing it's likelihood on reference CT data. Due to this unsupervised learning strategy, our trained regularizer truly represents higher-level domain statistics, which we empirically demonstrate by synthesizing CT images. Moreover, this regularizer can easily be applied to different CT reconstruction problems by embedding it in a variational framework, which increases flexibility and interpretability compared to feed-forward learning-based approaches. In addition, the accompanying probabilistic perspective enables experts to explore the full posterior distribution and may quantify uncertainty of the reconstruction approach. We apply the regularizer to limited-angle and few-view CT reconstruction problems, where it outperforms traditional reconstruction algorithms by a large margin.

Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and exploiting automatic differentiation in an end-to-end model-based gradient descent framework. This approach has proven effective for optimizing DRPs in nonlinear continuous MDPs, but it requires a large number of sampled trajectories to learn effectively and can suffer from high variance in solution quality. In this work, we revisit the overall model-based DRP objective and instead take a minorization-maximization perspective to iteratively optimize the DRP w.r.t. a locally tight lower-bounded objective. This novel formulation of DRP learning as iterative lower bound optimization (ILBO) is particularly appealing because (i) each step is structurally easier to optimize than the overall objective, (ii) it guarantees a monotonically improving objective under certain theoretical conditions, and (iii) it reuses samples between iterations thus lowering sample complexity. Empirical evaluation confirms that ILBO is significantly more sample-efficient than the state-of-the-art DRP planner and consistently produces better solution quality with lower variance. We additionally demonstrate that ILBO generalizes well to new problem instances (i.e., different initial states) without requiring retraining.

We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platform or loss of human life. Such problems are referred to as safe optimization problems (SafeOPs). While SafeOPs have received attention in the machine learning community in recent years, there was little interest in the evolutionary computation (EC) community despite some early attempts between 2009 and 2011. Moreover, there is a lack of acceptable guidelines on how to benchmark different algorithms for SafeOPs, an area where the EC community has significant experience in. Driven by the need for more efficient algorithms and benchmark guidelines for SafeOPs, the objective of this paper is to reignite the interest of this problem class in the EC community. To achieve this we (i) provide a formal definition of SafeOPs and contrast it to other types of optimization problems that the EC community is familiar with, (ii) investigate the impact of key SafeOP parameters on the performance of selected safe optimization algorithms, (iii) benchmark EC against state-of-the-art safe optimization algorithms from the machine learning community, and (iv) provide an open-source Python framework to replicate and extend our work.

Modern day computer games have extremely large state and action spaces. To detect bugs in these games' models, human testers play the games repeatedly to explore the game and find errors in the games. Such game play is exhaustive and time consuming. Moreover, since robotics simulators depend on similar methods of model specification and debugging, the problem of finding errors in the model is of interest for the robotics community to ensure robot behaviors and interactions are consistent in simulators. Previous methods have used reinforcement learning and search based methods including Rapidly-exploring Random Trees (RRT) to explore a game's state-action space to find bugs. However, such search and exploration based methods are not efficient at exploring the state-action space without a pre-defined heuristic. In this work we attempt to combine a human-tester's expertise in solving games, and the exhaustiveness of RRT to search a game's state space efficiently with high coverage. This paper introduces human-seeded RRT (HS-RRT) and behavior-cloning-assisted RRT (CA-RRT) in testing the number of game states searched and the time taken to explore those game states. We compare our methods to an existing weighted RRT baseline for game exploration testing studied. We find HS-RRT and CA-RRT both explore more game states in fewer tree expansions/iterations when compared to the existing baseline. In each test, CA-RRT reached more states on average in the same number of iterations as RRT. In our tested environments, CA-RRT was able to reach the same number of states as RRT by more than 5000 fewer iterations on average, almost a 50% reduction.

We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum annealers (QA). The computational premise of quantum computers has cultivated the re-design of various existing vision problems into quantum-friendly forms. Experimental QA realizations can solve a particular non-convex problem known as the quadratic unconstrained binary optimization (QUBO). Yet a naive-QUBO cannot take into account the restrictions on the parameters. To introduce additional structure in the parameter space, researchers have crafted ad-hoc solutions incorporating (linear) constraints in the form of regularizers. However, this comes at the expense of a hyper-parameter, balancing the impact of regularization. To date, a true constrained solver of quadratic binary optimization (QBO) problems has lacked. Q-FW first reformulates constrained-QBO as a copositive program (CP), then employs Frank-Wolfe iterations to solve CP while satisfying linear (in)equality constraints. This procedure unrolls the original constrained-QBO into a set of unconstrained QUBOs all of which are solved, in a sequel, on a QA. We use D-Wave Advantage QA to conduct synthetic and real experiments on two important computer vision problems, graph matching and permutation synchronization, which demonstrate that our approach is effective in alleviating the need for an explicit regularization coefficient.

We introduce a new reinforcement learning principle that approximates the Bellman equations by enforcing their validity only along an user-defined space of test functions. Focusing on applications to model-free offline RL with function approximation, we exploit this principle to derive confidence intervals for off-policy evaluation, as well as to optimize over policies within a prescribed policy class. We prove an oracle inequality on our policy optimization procedure in terms of a trade-off between the value and uncertainty of an arbitrary comparator policy. Different choices of test function spaces allow us to tackle different problems within a common framework. We characterize the loss of efficiency in moving from on-policy to off-policy data using our procedures, and establish connections to concentrability coefficients studied in past work. We examine in depth the implementation of our methods with linear function approximation, and provide theoretical guarantees with polynomial-time implementations even when Bellman closure does not hold.

Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of every high-level action in existence, but instead only consider ones they deem possible in the first place. By focusing only on what is feasible, or "afforded", at the present moment, an agent can spend more time both evaluating the utility of and acting on what matters. To this end, we present Hierarchical Affordance Learning (HAL), a method that learns a model of hierarchical affordances in order to prune impossible subtasks for more effective learning. Existing works in hierarchical reinforcement learning provide agents with structural representations of subtasks but are not affordance-aware, and by grounding our definition of hierarchical affordances in the present state, our approach is more flexible than the multitude of approaches that ground their subtask dependencies in a symbolic history. While these logic-based methods often require complete knowledge of the subtask hierarchy, our approach is able to utilize incomplete and varying symbolic specifications. Furthermore, we demonstrate that relative to non-affordance-aware methods, HAL agents are better able to efficiently learn complex tasks, navigate environment stochasticity, and acquire diverse skills in the absence of extrinsic supervision -- all of which are hallmarks of human learning.

Machine learning algorithms can perform well when trained on large datasets. While large organisations often have considerable data assets, it can be difficult for these assets to be unified in a manner that makes training possible. Data is very often 'siloed' in different parts of the organisation, with little to no access between silos. This fragmentation of data assets is especially prevalent in heavily regulated industries like financial services or healthcare. In this paper we propose a framework to enable asynchronous collaborative training of machine learning models across data silos. This allows data science teams to collaboratively train a machine learning model, without sharing data with one another. Our proposed approach enhances conventional federated learning techniques to make them suitable for this asynchronous training in this intra-organisation, cross-silo setting. We validate our proposed approach via extensive experiments.

We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies. The datasets contain hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories. While public datasets from multiple fundamental physics disciplines already exist, the common interface and provided reference models simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. We discuss the design and structure and line out how additional datasets can be submitted for inclusion. As showcase application, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks. We show that our approach reaches performance close to dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.

Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. Several variants of CCA have been introduced in the literature, in particular, variants based on deep neural networks for learning highly correlated nonlinear transformations of two views. As these models are parameterized conventionally, their learnable parameters remain independent of the inputs after the training process, which may limit their capacity for learning highly correlated representations. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model. In our deep-CCA models, the parameters of the last layer are scaled by a second neural network that is conditioned on the model's input, resulting in a parameterization that is dependent on the input samples. We evaluate our model on multiple datasets and demonstrate that the learned representations are more correlated in comparison to the conventionally-parameterized CCA-based models and also obtain preferable retrieval results. Our code is available at https://github.com/tomerfr/DynamicallyScaledDeepCCA.

Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature attribution algorithms treat all features in a dataset homogeneously, which may lead to misinterpretation of consequences of changing feature values. In this work, we consider partitioning features into controllable and uncontrollable parts and propose the Controllable fActor Feature Attribution (CAFA) approach to compute the relative importance of controllable features. We carried out experiments applying CAFA to two existing datasets and our own COVID-19 non-pharmaceutical control measures dataset. Experimental results show that with CAFA, we are able to exclude influences from uncontrollable features in our explanation while keeping the full dataset for prediction.

I present a Variational Autoencoder (VAE) trained on collider physics data (specifically boosted $W$ jets), with reconstruction error given by an approximation to the Earth Movers Distance (EMD) between input and output jets. This VAE learns a concrete representation of the data manifold, with semantically meaningful and interpretable latent space directions which are hierarchically organized in terms of their relation to physical EMD scales in the underlying physical generative process. A hyperparameter $\beta$ controls the resolution at which the VAE is sensitive to structures in the data manifold. The variation of the latent space structure with $\beta$, and the scaling of some VAE properties, provide insight into scale dependent structure of the dataset and its information complexity. I introduce two measures of the dimensionality of the learnt representation that are calculated from this scaling.

Fairness emerged as an important requirement to guarantee that Machine Learning (ML) predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey that illustrates the subtleties between fairness notions through a large number of examples and scenarios. In addition, unlike other surveys in the literature, it addresses the question of: which notion of fairness is most suited to a given real-world scenario and why? Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policymakers to navigate the relatively large catalog of ML.

Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Therefore, accurate and early detection of PH is crucial for successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. In this work, we present an interpretable multi-view video-based deep learning approach to predict PH for a cohort of 194 newborns using echocardiograms. We use spatio-temporal convolutional architectures for the prediction of PH from each view, and aggregate the predictions of the different views using majority voting. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation. We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice.

Learning in biological and artificial neural networks is often framed as a problem in which targeted error signals guide parameter updating for more optimal network behaviour. Backpropagation of error (BP) is an example of such an approach and has proven to be a highly successful application of stochastic gradient descent to deep neural networks. However, BP relies on the global transmission of gradient information and has therefore been criticised for its biological implausibility. We propose constrained parameter inference (COPI) as a new principle for learning. COPI allows for the estimation of network parameters under the constraints of decorrelated neural inputs and top-down perturbations of neural states. We show that COPI not only is more biologically plausible but also provides distinct advantages for fast learning, compared with standard backpropagation of error.

Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize all operations into an augmented network, including those that rarely contribute to the model's performance. As such, the price to pay is an expensive computational overhead to manipulate these unnecessary behaviors. To eliminate the above caveats, we aim to bootstrap the training with minimal cost by devising a dynamic re-parameterization (DyRep) method, which encodes Rep technique into the training process that dynamically evolves the network structures. Concretely, our proposal adaptively finds the operations which contribute most to the loss in the network, and applies Rep to enhance their representational capacity. Besides, to suppress the noisy and redundant operations introduced by Rep, we devise a de-parameterization technique for a more compact re-parameterization. With this regard, DyRep is more efficient than Rep since it smoothly evolves the given network instead of constructing an over-parameterized network. Experimental results demonstrate our effectiveness, e.g., DyRep improves the accuracy of ResNet-18 by $2.04\%$ on ImageNet and reduces $22\%$ runtime over the baseline. Code is available at: https://github.com/hunto/DyRep.

Text-to-image synthesis (T2I) aims to generate photo-realistic images which are semantically consistent with the text descriptions. Existing methods are usually built upon conditional generative adversarial networks (GANs) and initialize an image from noise with sentence embedding, and then refine the features with fine-grained word embedding iteratively. A close inspection of their generated images reveals a major limitation: even though the generated image holistically matches the description, individual image regions or parts of somethings are often not recognizable or consistent with words in the sentence, e.g. "a white crown". To address this problem, we propose a novel framework Semantic-Spatial Aware GAN for synthesizing images from input text. Concretely, we introduce a simple and effective Semantic-Spatial Aware block, which (1) learns semantic-adaptive transformation conditioned on text to effectively fuse text features and image features, and (2) learns a semantic mask in a weakly-supervised way that depends on the current text-image fusion process in order to guide the transformation spatially. Experiments on the challenging COCO and CUB bird datasets demonstrate the advantage of our method over the recent state-of-the-art approaches, regarding both visual fidelity and alignment with input text description.  ( 2 min )

Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs. This allows a wide variety of high- and low-level physical features to be attached to measurements and, by the same token, a wide variety of HEP tasks to be accomplished by the same GNN architectures. GNNs have found powerful use-cases in reconstruction, tagging, generation and end-to-end analysis. With the wide-spread adoption of GNNs in industry, the HEP community is well-placed to benefit from rapid improvements in GNN latency and memory usage. However, industry use-cases are not perfectly aligned with HEP and much work needs to be done to best match unique GNN capabilities to unique HEP obstacles. We present here a range of these capabilities, predictions of which are currently being well-adopted in HEP communities, and which are still immature. We hope to capture the landscape of graph techniques in machine learning as well as point out the most significant gaps that are inhibiting potentially large leaps in research.  ( 2 min )

Training of speech enhancement systems often does not incorporate knowledge of human perception and thus can lead to unnatural sounding results. Incorporating psychoacoustically motivated speech perception metrics as part of model training via a predictor network has recently gained interest. However, the performance of such predictors is limited by the distribution of metric scores that appear in the training data. In this work, we propose MetricGAN+/- (an extension of MetricGAN+, one such metric-motivated system) which introduces an additional network - a "de-generator" which attempts to improve the robustness of the prediction network (and by extension of the generator) by ensuring observation of a wider range of metric scores in training. Experimental results on the VoiceBank-DEMAND dataset show relative improvement in PESQ score of 3.8% (3.05 vs 3.22 PESQ score), as well as better generalisation to unseen noise and speech.

Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in form-like documents in practice due to their variety of layout patterns. We propose FormNet, a structure-aware sequence model to mitigate the suboptimal serialization of forms. First, we design Rich Attention that leverages the spatial relationship between tokens in a form for more precise attention score calculation. Second, we construct Super-Tokens for each word by embedding representations from their neighboring tokens through graph convolutions. FormNet therefore explicitly recovers local syntactic information that may have been lost during serialization. In experiments, FormNet outperforms existing methods with a more compact model size and less pre-training data, establishing new state-of-the-art performance on CORD, FUNSD and Payment benchmarks.  ( 2 min )

Purpose: A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example as per RECIST or RANO criteria, is tedious, time-consuming, and can miss important tumor response information; most notably, they exclude non-target lesions. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Due to often low patient enrolments in clinical trials, we wish to make response assessments with small training sets. Deep neuroevolution (DNE) can produce radiology artificial intelligence (AI) that performs well on small training sets. Here we use DNE for function approximation that predicts progression versus regression of metastatic brain disease. Methods: We analyzed 50 pairs of MRI contrast-enhanced images as our training set. Half of these pairs, separated in time, qualified as disease progression, while the other 25 images constituted regression. We trained the parameters of a relatively small CNN via mutations that consisted of random CNN weight adjustments and mutation fitness. We then incorporated the best mutations into the next generations CNN, repeating this process for approximately 50,000 generations. We applied the CNNs to our training set, as well as a separate testing set with the same class balance of 25 progression and 25 regression images. Results: DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. Conclusion: DNE can accurately classify brain-metastatic disease progression versus regression. Future work will extend the input from 2D image slices to full 3D volumes, and include the category of no change. We believe that an approach such as our could ultimately provide a useful adjunct to RANO/RECIST assessment.

Bayesian optimization is a gold standard for query-efficient continuous optimization. However, its adoption for drug and antibody sequence design has been hindered by the discrete, high-dimensional nature of the decision variables. We develop a new approach (LaMBO) which jointly trains a denoising autoencoder with a discriminative multi-task Gaussian process head, enabling gradient-based optimization of multi-objective acquisition functions in the latent space of the autoencoder. These acquisition functions allow LaMBO to balance the explore-exploit trade-off over multiple design rounds, and to balance objective tradeoffs by optimizing sequences at many different points on the Pareto frontier. We evaluate LaMBO on a small-molecule task based on the ZINC dataset and introduce a new large-molecule task targeting fluorescent proteins. In our experiments, LaMBO outperforms genetic optimizers and does not require a large pretraining corpus, demonstrating that Bayesian optimization is practical and effective for biological sequence design.

Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level embeddings by maximizing the Mutual Information (MI) between different augmented views of the same graph (positive pairs). However, we identify an obstacle that the optimization of InfoNCE loss only concentrates on a few embeddings dimensions, limiting the distinguishability of embeddings in downstream graph classification tasks. This paper proposes an effective graph complementary contrastive learning approach named GraphCoCo to tackle the above issue. Specifically, we set the embedding of the first augmented view as the anchor embedding to localize "highlighted" dimensions (i.e., the dimensions contribute most in similarity measurement). Then remove these dimensions in the embeddings of the second augmented view to discover neglected complementary representations. Therefore, the combination of anchor and complementary embeddings significantly improves the performance in downstream tasks. Comprehensive experiments on various benchmark datasets are conducted to demonstrate the effectiveness of GraphCoCo, and the results show that our model outperforms the state-of-the-art methods. Source code will be made publicly available.

The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from data for explicitly parameterized models, which however are not appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we propose the first machine unlearning algorithm for MCMC. We first convert the MCMC unlearning problem into an explicit optimization problem. Based on this problem conversion, an {\it MCMC influence function} is designed to provably characterize the learned knowledge from data, which then delivers the MCMC unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not compromise the generalizability of the MCMC models. Experiments on Gaussian mixture models and Bayesian neural networks confirm the effectiveness of the proposed algorithm. The code is available at \url{https://github.com/fshp971/mcmc-unlearning}.

Estimation of human shape and pose from a single image is a challenging task. It is an even more difficult problem to map the identified human shape onto a 3D human model. Existing methods map manually labelled human pixels in real 2D images onto the 3D surface, which is prone to human error, and the sparsity of available annotated data often leads to sub-optimal results. We propose to solve the problem of data scarcity by training 2D-3D human mapping algorithms using automatically generated synthetic data for which exact and dense 2D-3D correspondence is known. Such a learning strategy using synthetic environments has a high generalisation potential towards real-world data. Using different camera parameter variations, background and lighting settings, we created precise ground truth data that constitutes a wider distribution. We evaluate the performance of models trained on synthetic using the COCO dataset and validation framework. Results show that training 2D-3D mapping network models on synthetic data is a viable alternative to using real data.  ( 2 min )

High-quality system-level message flow specifications can lead to comprehensive validation of system-on-chip (SoC) designs. We propose a disruptive method that utilizes an attention mechanism to produce accurate flow specifications from SoC IP communication traces. The proposed method can overcome the inherent complexity of SoC traces induced by the concurrency and parallelism of multicore designs that existing flow specification mining tools often find extremely challenging. Experiments on highly interleaved traces show promising flow reconstruction compared to several tools dedicated to the flow specification mining problem.  ( 2 min )

Non-convex loss functions arise frequently in modern machine learning, and for the theoretical analysis of stochastic optimization methods, the presence of non-isolated minima presents a unique challenge that has remained under-explored. In this paper, we study the local convergence of the stochastic gradient descent method to non-isolated global minima. Under mild assumptions, we estimate the probability for the iterations to stay near the minima by adopting the notion of stochastic stability. After establishing such stability, we present the lower bound complexity in terms of various error criteria for a given error tolerance $\epsilon$ and a failure probability $\gamma$.  ( 2 min )

We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how polynomiality can remedy the situation. This, among others, leads purposefully to random-level performance in the tails, away from the bulk of the training data. A directly related, simple, yet important technical novelty we subsequently present is softRmax: a reasoned alternative to the standard softmax function employed in contemporary (deep) neural networks. It is derived through linking the standard softmax to Gaussian class-conditional models, as employed in LDA, and replacing those by a polynomial alternative. We show that two aspects of softRmax, conservativeness and inherent gradient regularization, lead to robustness against adversarial attacks without gradient obfuscation.

Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifact, denoising is largely studied both within the medical imaging community and beyond the community as a general subject. However, recent deep neural network-based approaches mostly rely on the minimum mean squared error (MMSE) estimates, which tend to produce a blurred output. Moreover, such models suffer when deployed in real-world sitautions: out-of-distribution data, and complex noise distributions that deviate from the usual parametric noise models. In this work, we propose a new denoising method based on score-based reverse diffusion sampling, which overcomes all the aforementioned drawbacks. Our network, trained only with coronal knee scans, excels even on out-of-distribution in vivo liver MRI data, contaminated with complex mixture of noise. Even more, we propose a method to enhance the resolution of the denoised image with the same network. With extensive experiments, we show that our method establishes state-of-the-art performance, while having desirable properties which prior MMSE denoisers did not have: flexibly choosing the extent of denoising, and quantifying uncertainty.

We study how the choice of visual perspective affects learning and generalization in the context of physical manipulation from raw sensor observations. Compared with the more commonly used global third-person perspective, a hand-centric (eye-in-hand) perspective affords reduced observability, but we find that it consistently improves training efficiency and out-of-distribution generalization. These benefits hold across a variety of learning algorithms, experimental settings, and distribution shifts, and for both simulated and real robot apparatuses. However, this is only the case when hand-centric observability is sufficient; otherwise, including a third-person perspective is necessary for learning, but also harms out-of-distribution generalization. To mitigate this, we propose to regularize the third-person information stream via a variational information bottleneck. On six representative manipulation tasks with varying hand-centric observability adapted from the Meta-World benchmark, this results in a state-of-the-art reinforcement learning agent operating from both perspectives improving its out-of-distribution generalization on every task. While some practitioners have long put cameras in the hands of robots, our work systematically analyzes the benefits of doing so and provides simple and broadly applicable insights for improving end-to-end learned vision-based robotic manipulation.  ( 2 min )

Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. Drug efficacy measurement through assessment of viability of onchocerca worms requires the patients to undergo nodulectomy which is invasive, expensive, time-consuming, skill-dependent, infrastructure dependent and lengthy process. In this paper, we discuss the first-ever study that proposes use of machine learning over thermal imaging to non-invasively and accurately predict the viability of worms. The key contributions of the paper are (i) a unique thermal imaging protocol along with pre-processing steps such as alignment, registration and segmentation to extract interpretable features (ii) extraction of relevant semantic features (iii) development of accurate classifiers for detecting the existence of viable worms in a nodule. When tested on a prospective test data of 30 participants with 48 palpable nodules, we achieved an Area Under the Curve (AUC) of 0.85.

Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns in the practical operational environment, which calls for systematic testing, especially in safety-critical scenarios. Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs. However, due to the blackbox nature of DNN, the existing structural coverage criteria are difficult to interpret, making it hard to understand the underlying principles of these criteria. The relationship between the structural coverage and the decision logic of DNNs is unknown. Moreover, recent studies have further revealed the non-existence of correlation between the structural coverage and DNN defect detection, which further posts concerns on what a suitable DNN testing criterion should be. In this paper, we propose the interpretable coverage criteria through constructing the decision structure of a DNN. Mirroring the control flow graph of the traditional program, we first extract a decision graph from a DNN based on its interpretation, where a path of the decision graph represents a decision logic of the DNN. Based on the control flow and data flow of the decision graph, we propose two variants of path coverage to measure the adequacy of the test cases in exercising the decision logic. The higher the path coverage, the more diverse decision logic the DNN is expected to be explored. Our large-scale evaluation results demonstrate that: the path in the decision graph is effective in characterizing the decision of the DNN, and the proposed coverage criteria are also sensitive with errors including natural errors and adversarial examples, and strongly correlated with the output impartiality.  ( 3 min )

Organizations have realized the importance of data analysis and its benefits. This in combination with Machine Learning algorithms has allowed to solve problems more easily, making these processes less time-consuming. Neural networks are the Machine Learning technique that is recently obtaining very good best results. This paper describes an open-source Python library called GEMA developed to work with a type of neural network model called Self-Organizing-Maps. GEMA is freely available under GNU General Public License at GitHub (https://github.com/ufvceiec/GEMA). The library has been evaluated in different a particular use case obtaining accurate results.  ( 2 min )

We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning problems whose state-action space is endowed with a metric. We introduce Kernel-UCBVI, a model-based optimistic algorithm that leverages the smoothness of the MDP and a non-parametric kernel estimator of the rewards and transitions to efficiently balance exploration and exploitation. For problems with $K$ episodes and horizon $H$, we provide a regret bound of $\widetilde{O}\left( H^3 K^{\frac{2d}{2d+1}}\right)$, where $d$ is the covering dimension of the joint state-action space. This is the first regret bound for kernel-based RL using smoothing kernels, which requires very weak assumptions on the MDP and has been previously applied to a wide range of tasks. We empirically validate our approach in continuous MDPs with sparse rewards.  ( 2 min )

In this paper we investigate the problem of stochastic multi-armed bandits (MAB) in the (local) differential privacy (DP/LDP) model. Unlike previous results that assume bounded/sub-Gaussian reward distributions, we focus on the setting where each arm's reward distribution only has $(1+v)$-th moment with some $v\in (0, 1]$. In the first part, we study the problem in the central $\epsilon$-DP model. We first provide a near-optimal result by developing a private and robust Upper Confidence Bound (UCB) algorithm. Then, we improve the result via a private and robust version of the Successive Elimination (SE) algorithm. Finally, we establish the lower bound to show that the instance-dependent regret of our improved algorithm is optimal. In the second part, we study the problem in the $\epsilon$-LDP model. We propose an algorithm that can be seen as locally private and robust version of SE algorithm, which provably achieves (near) optimal rates for both instance-dependent and instance-independent regret. Our results reveal differences between the problem of private MAB with bounded/sub-Gaussian rewards and heavy-tailed rewards. To achieve these (near) optimal rates, we develop several new hard instances and private robust estimators as byproducts, which might be used to other related problems. Finally, experiments also support our theoretical findings and show the effectiveness of our algorithms.  ( 2 min )

Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches. This paper proposes ColBERTer, a neural retrieval model using contextualized late interaction (ColBERT) with enhanced reduction. Along the effectiveness Pareto frontier, ColBERTer's reductions dramatically lower ColBERT's storage requirements while simultaneously improving the interpretability of its token-matching scores. To this end, ColBERTer fuses single-vector retrieval, multi-vector refinement, and optional lexical matching components into one model. For its multi-vector component, ColBERTer reduces the number of stored vectors per document by learning unique whole-word representations for the terms in each document and learning to identify and remove word representations that are not essential to effective scoring. We employ an explicit multi-task, multi-stage training to facilitate using very small vector dimensions. Results on the MS MARCO and TREC-DL collection show that ColBERTer can reduce the storage footprint by up to 2.5x, while maintaining effectiveness. With just one dimension per token in its smallest setting, ColBERTer achieves index storage parity with the plaintext size, with very strong effectiveness results. Finally, we demonstrate ColBERTer's robustness on seven high-quality out-of-domain collections, yielding statistically significant gains over traditional retrieval baselines.  ( 2 min )

Although Shapley Values (SV) are widely used in explainable AI, they can be poorly understood and estimated, implying that their analysis may lead to spurious inferences and explanations. As a starting point, we remind an invariance principle for SV and derive the correct approach for computing the SV of categorical variables that are particularly sensitive to the encoding used. In the case of tree-based models, we introduce two estimators of Shapley Values that exploit the tree structure efficiently and are more accurate than state-of-the-art methods. Simulations and comparisons are performed with state-of-the-art algorithms and show the practical gain of our approach. Finally, we discuss the ability of SV to provide reliable local explanations. We also provide a Python package that computes our estimators at https://github.com/salimamoukou/acv00.  ( 2 min )

We study a finite-horizon restless multi-armed bandit problem with multiple actions, dubbed R(MA)^2B. The state of each arm evolves according to a controlled Markov decision process (MDP), and the reward of pulling an arm depends on both the current state of the corresponding MDP and the action taken. The goal is to sequentially choose actions for arms so as to maximize the expected value of the cumulative rewards collected. Since finding the optimal policy is typically intractable, we propose a computationally appealing index policy which we call Occupancy-Measured-Reward Index Policy. Our policy is well-defined even if the underlying MDPs are not indexable. We prove that it is asymptotically optimal when the activation budget and number of arms are scaled up, while keeping their ratio as a constant. For the case when the system parameters are unknown, we develop a learning algorithm. Our learning algorithm uses the principle of optimism in the face of uncertainty and further uses a generative model in order to fully exploit the structure of Occupancy-Measured-Reward Index Policy. We call it the R(MA)^2B-UCB algorithm. As compared with the existing algorithms, R(MA)^2B-UCB performs close to an offline optimum policy, and also achieves a sub-linear regret with a low computational complexity. Experimental results show that R(MA)^2B-UCB outperforms the existing algorithms in both regret and run time.  ( 2 min )

We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector. The method maximizes an achievable information rate, while simultaneously satisfying constraints on the adjacent channel leakage ratio (ACLR) and peak-to-average power ratio (PAPR). This allows control of the tradeoff between spectral containment, peak power, and communication rate. Evaluation on an additive white Gaussian noise (AWGN) channel shows significant reduction of ACLR and PAPR compared to a conventional baseline relying on quadrature amplitude modulation (QAM) and root-raised-cosine (RRC), without significant loss of information rate. When considering a 3rd Generation Partnership Project (3GPP) multipath channel, the learned waveform and neural receiver enable competitive or higher rates than an orthogonal frequency division multiplexing (OFDM) baseline, while reducing the ACLR by 10 dB and the PAPR by 2 dB. The proposed method incurs no additional complexity on the transmitter side and might be an attractive tool for waveform design of beyond-5G systems.  ( 2 min )

State-of-the-art automatic speech recognition (ASR) systems are trained with tens of thousands of hours of labeled speech data. Human transcription is expensive and time consuming. Factors such as the quality and consistency of the transcription can greatly affect the performance of the ASR models trained with these data. In this paper, we show that we can train a strong teacher model to produce high quality pseudo labels by utilizing recent self-supervised and semi-supervised learning techniques. Specifically, we use JUST (Joint Unsupervised/Supervised Training) and iterative noisy student teacher training to train a 600 million parameter bi-directional teacher model. This model achieved 4.0% word error rate (WER) on a voice search task, 11.1% relatively better than a baseline. We further show that by using this strong teacher model to generate high-quality pseudo labels for training, we can achieve 13.6% relative WER reduction (5.9% to 5.1%) for a streaming model compared to using human labels.  ( 2 min )

This study addresses a multiclass learning from label proportions (MCLLP) setting in which training instances are provided in bags and only the proportion of each class within the bags is provided. Most existing MCLLP methods impose bag-wise constraints on the prediction of instances or assign them pseudo-labels; however, none of these methods have a theoretical consistency. To solve this problem, a risk-consistent method is proposed for instance classification using the empirical risk minimization framework, and its estimation error bound is derived. An approximation method is proposed for the proposed risk estimator, to apply it to large bags, by diverting the constraints on bags in existing research. The proposed method can be applied to any deep model or loss and is compatible with stochastic optimization. Experiments are conducted on benchmarks to verify the effectiveness of the proposed method.  ( 2 min )

COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for computer-assisted diagnosis. However, there are two most significant challenges in medical image classification with the help of deep learning and neural networks, one of them is the difficulty of acquiring enough samples, which may lead to model overfitting. Privacy concerns mainly bring the other challenge since medical-related records are often deemed patients' private information and protected by laws such as GDPR and HIPPA. Federated learning can ensure the model training is decentralized on different devices and no data is shared among them, which guarantees privacy. However, with data located on different devices, the accessible data of each device could be limited. Since transfer learning has been verified in dealing with limited data with good performance, therefore, in this paper, We made a trial to implement federated learning and transfer learning techniques using CNNs to classify COVID-19 using lung CT scans. We also explored the impact of dataset distribution at the client-side in federated learning and the number of training epochs a model is trained. Finally, we obtained very high performance with federated learning, demonstrating our success in leveraging accuracy and privacy.  ( 3 min )

Transformer has brought great success to a wide range of natural language processing tasks. Nevertheless, the computational overhead of the vanilla transformer scales quadratically with sequence length. Many efforts have been made to develop more efficient transformer variants. A line of work (e.g., Linformer) projects the input sequence into a low-rank space, achieving linear time complexity. However, Linformer does not suit well for text generation tasks as the sequence length must be pre-specified. We propose MemSizer, an approach also projects the source sequence into lower dimension representation but can take input with dynamic length, with a different perspective of the attention mechanism. MemSizer not only achieves the same linear time complexity but also enjoys efficient recurrent-style autoregressive generation, which yields constant memory complexity and reduced computation at inference. We demonstrate that MemSizer provides an improved tradeoff between efficiency and accuracy over the vanilla transformer and other linear variants in language modeling and machine translation tasks, revealing a viable direction towards further inference efficiency improvement.  ( 2 min )

Given an irregular dense tensor, how can we efficiently analyze it? An irregular tensor is a collection of matrices whose columns have the same size and rows have different sizes from each other. PARAFAC2 decomposition is a fundamental tool to deal with an irregular tensor in applications including phenotype discovery and trend analysis. Although several PARAFAC2 decomposition methods exist, their efficiency is limited for irregular dense tensors due to the expensive computations involved with the tensor. In this paper, we propose DPar2, a fast and scalable PARAFAC2 decomposition method for irregular dense tensors. DPar2 achieves high efficiency by effectively compressing each slice matrix of a given irregular tensor, careful reordering of computations with the compression results, and exploiting the irregularity of the tensor. Extensive experiments show that DPar2 is up to 6.0x faster than competitors on real-world irregular tensors while achieving comparable accuracy. In addition, DPar2 is scalable with respect to the tensor size and target rank.  ( 2 min )

For autonomous agents to act as trustworthy partners to human users, they must be able to reliably communicate their competency for the tasks they are asked to perform. Towards this objective, we develop probabilistic world models based on deep generative modelling that allow for the simulation of agent trajectories and accurate calculation of tasking outcome probabilities. By combining the strengths of conditional variational autoencoders with recurrent neural networks, the deep generative world model can probabilistically forecast trajectories over long horizons to task completion. We show how these forecasted trajectories can be used to calculate outcome probability distributions, which enable the precise assessment of agent competency for specific tasks and initial settings.  ( 2 min )

We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential equations that are ubiquitous in the study and modeling of physics phenomena. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our physics-informed neural operators to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled partial differential equations. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code, an interactive website to visualize the predictions of our physics informed neural operators, and a tutorial for their use at the Data and Learning Hub for Science.  ( 2 min )

The Internet of Things (IoT) has been continuously rising in the past few years, and its potentials are now more apparent. However, transient data generation and limited energy resources are the major bottlenecks of these networks. Besides, minimum delay and other conventional quality of service measurements are still valid requirements to meet. An efficient caching policy can help meet the standard quality of service requirements while bypassing IoT networks' specific limitations. Adopting deep reinforcement learning (DRL) algorithms enables us to develop an effective caching scheme without the need for any prior knowledge or contextual information. In this work, we propose a DRL-based caching scheme that improves the cache hit rate and reduces energy consumption of the IoT networks, in the meanwhile, taking data freshness and limited lifetime of IoT data into account. To better capture the regional-different popularity distribution, we propose a hierarchical architecture to deploy edge caching nodes in IoT networks. The results of comprehensive experiments show that our proposed method outperforms the well-known conventional caching policies and an existing DRL-based solution in terms of cache hit rate and energy consumption of the IoT networks by considerable margins.  ( 2 min )

Current domain adaptation methods address the problems of covariate shift or label shift, but are not applicable to the setting where they occur simultaneously and interact with each other. In this paper, we propose an assumption, confounded shift, to begin to address this problem. We also propose a framework for this task, based on minimizing the expected divergence between the source and target conditional distributions. Within this framework, we propose using the reverse KL divergence, demonstrating the use of both parametric linear Gaussian and nonparametric nonlinear Gaussian Process estimators of the conditional distribution. We also propose using the Maximum Mean Discrepancy (MMD) within our framework. To make confounded domain adaptation with the MMD effective, we propose an intelligent dynamic strategy for choosing the kernel bandwidth, which may be of independent interest even outside of the confounded shift context. Finally, we show that our approach is advantageous on a variety of synthetic and real datasets.  ( 2 min )

An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel prediction algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long-short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.  ( 2 min )

Increasingly larger and better Transformer models keep advancing state-of-the-art accuracy and capability for Natural Language Processing applications. These models demand more computational power, storage, and energy. Mokey reduces the footprint of state-of-the-art 32-bit or 16-bit floating-point transformer models by quantizing all values to 4-bit indexes into dictionaries of representative 16-bit fixed-point centroids. Mokey does not need fine-tuning, an essential feature as often the training resources or datasets are not available to many. Exploiting the range of values that naturally occur in transformer models, Mokey selects centroid values to also fit an exponential curve. This unique feature enables Mokey to replace the bulk of the original multiply-accumulate operations with narrow 3b fixed-point additions resulting in an area- and energy-efficient hardware accelerator design. Over a set of state-of-the-art transformer models, the Mokey accelerator delivers an order of magnitude improvements in energy efficiency over a Tensor Cores-based accelerator while improving performance by at least $4\times$ and as much as $15\times$ depending on the model and on-chip buffering capacity. Optionally, Mokey can be used as a memory compression assist for any other accelerator, transparently stashing wide floating-point or fixed-point activations or weights into narrow 4-bit indexes. Mokey proves superior to prior state-of-the-art quantization methods for Transformers.  ( 2 min )

A long-term goal of AI research is to build intelligent agents that can communicate with humans in natural language, perceive the environment, and perform real-world tasks. Vision-and-Language Navigation (VLN) is a fundamental and interdisciplinary research topic towards this goal, and receives increasing attention from natural language processing, computer vision, robotics, and machine learning communities. In this paper, we review contemporary studies in the emerging field of VLN, covering tasks, evaluation metrics, methods, etc. Through structured analysis of current progress and challenges, we highlight the limitations of current VLN and opportunities for future work. This paper serves as a thorough reference for the VLN research community.  ( 2 min )

Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks in representation learning - the need for human annotations. As a result, SSL holds the promise to learn representations from data in-the-wild, i.e., without the need for finite and static datasets. Instead, true SSL algorithms should be able to exploit the continuous stream of data being generated on the internet or by agents exploring their environments. But do traditional self-supervised learning approaches work in this setup? In this work, we investigate this question by conducting experiments on the continuous self-supervised learning problem. While learning in the wild, we expect to see a continuous (infinite) non-IID data stream that follows a non-stationary distribution of visual concepts. The goal is to learn a representation that can be robust, adaptive yet not forgetful of concepts seen in the past. We show that a direct application of current methods to such continuous setup is 1) inefficient both computationally and in the amount of data required, 2) leads to inferior representations due to temporal correlations (non-IID data) in some sources of streaming data and 3) exhibits signs of catastrophic forgetting when trained on sources with non-stationary data distributions. We propose the use of replay buffers as an approach to alleviate the issues of inefficiency and temporal correlations. We further propose a novel method to enhance the replay buffer by maintaining the least redundant samples. Minimum redundancy (MinRed) buffers allow us to learn effective representations even in the most challenging streaming scenarios composed of sequential visual data obtained from a single embodied agent, and alleviates the problem of catastrophic forgetting when learning from data with non-stationary semantic distributions.  ( 2 min )

The problem of Reinforcement Learning (RL) in an unknown nonlinear dynamical system is equivalent to the search for an optimal feedback law utilizing the simulations/ rollouts of the dynamical system. Most RL techniques search over a complex global nonlinear feedback parametrization making them suffer from high training times as well as variance. Instead, we advocate searching over a local feedback representation consisting of an open-loop sequence, and an associated optimal linear feedback law completely determined by the open-loop. We show that this alternate approach results in highly efficient training, the answers obtained are repeatable and hence reliable, and the resulting closed performance is superior to global state-of-the-art RL techniques. Finally, if we replan, whenever required, which is feasible due to the fast and reliable local solution, it allows us to recover global optimality of the resulting feedback law.

Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations. There has been much work on improving generalization of DML in settings like zero-shot retrieval, but little is known about its implications for fairness. In this paper, we are the first to evaluate state-of-the-art DML methods trained on imbalanced data, and to show the negative impact these representations have on minority subgroup performance when used for downstream tasks. In this work, we first define fairness in DML through an analysis of three properties of the representation space -- inter-class alignment, intra-class alignment, and uniformity -- and propose finDML, the fairness in non-balanced DML benchmark to characterize representation fairness. Utilizing finDML, we find bias in DML representations to propagate to common downstream classification tasks. Surprisingly, this bias is propagated even when training data in the downstream task is re-balanced. To address this problem, we present Partial Attribute De-correlation (PARADE) to de-correlate feature representations from sensitive attributes and reduce performance gaps between subgroups in both embedding space and downstream metrics.  ( 2 min )

Zero-shot slot filling has received considerable attention to cope with the problem of limited available data for the target domain. One of the important factors in zero-shot learning is to make the model learn generalized and reliable representations. For this purpose, we present mcBERT, which stands for momentum contrastive learning with BERT, to develop a robust zero-shot slot filling model. mcBERT uses BERT to initialize the two encoders, the query encoder and key encoder, and is trained by applying momentum contrastive learning. Our experimental results on the SNIPS benchmark show that mcBERT substantially outperforms the previous models, recording a new state-of-the-art. Besides, we also show that each component composing mcBERT contributes to the performance improvement.

Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision. While the success of WS relies heavily on the provided labeling heuristics, the process of how these heuristics are created in practice has remained under-explored. In this work, we formalize the development process of labeling heuristics as an interactive procedure, built around the existing workflow where users draw ideas from a selected set of development data for designing the heuristic sources. With the formalism, we study two core problems of how to strategically select the development data to guide users in efficiently creating informative heuristics, and how to exploit the information within the development process to contextualize and better learn from the resultant heuristics. Building upon two novel methodologies that effectively tackle the respective problems considered, we present Nemo, an end-to-end interactive system that improves the overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS approach.

We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. By using the quantum computer as a processor for probability distributions, a probabilistic traversal of the decision tree can be realized via measurements of a quantum circuit. We describe how tree inductions and the prediction of class labels of query data can be integrated into this framework. An on-demand sampling method enables predictions with a constant number of classical memory slots, independent of the tree depth. We experimentally study our approach using both a quantum computing simulator and actual IBM quantum hardware. To our knowledge, this is the first realization of a decision tree classifier on a quantum device.  ( 2 min )

Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. In the proposed AVDCNN SE model, audio and visual data are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at the output layer. The AVDCNN model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation. We evaluate enhanced speech using five objective criteria. Results show that the AVDCNN yields notably better performance, compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process.  ( 2 min )

Variational representations of divergences and distances between high-dimensional probability distributions offer significant theoretical insights and practical advantages in numerous research areas. Recently, they have gained popularity in machine learning as a tractable and scalable approach for training probabilistic models and for statistically differentiating between data distributions. Their advantages include: 1) They can be estimated from data as statistical averages. 2) Such representations can leverage the ability of neural networks to efficiently approximate optimal solutions in function spaces. However, a systematic and practical approach to improving the tightness of such variational formulas, and accordingly accelerate statistical learning and estimation from data, is currently lacking. Here we develop such a methodology for building new, tighter variational representations of divergences. Our approach relies on improved objective functionals constructed via an auxiliary optimization problem. Furthermore, the calculation of the functional Hessian of objective functionals unveils the local curvature differences around the common optimal variational solution; this quantifies and orders the tightness gains between different variational representations. Finally, numerical simulations utilizing neural network optimization demonstrate that tighter representations can result in significantly faster learning and more accurate estimation of divergences in both synthetic and real datasets (of more than 1000 dimensions), often accelerated by nearly an order of magnitude.  ( 2 min )

Novel prediction methods should always be compared to a baseline to know how well they perform. Without this frame of reference, the performance score of a model is basically meaningless. What does it mean when a model achieves an $F_1$ of 0.8 on a test set? A proper baseline is needed to evaluate the `goodness' of a performance score. Comparing with the latest state-of-the-art model is usually insightful. However, being state-of-the-art can change rapidly when newer models are developed. Contrary to an advanced model, a simple dummy classifier could be used. However, the latter could be beaten too easily, making the comparison less valuable. This paper presents a universal baseline method for all binary classification models, named the Dutch Draw (DD). This approach weighs simple classifiers and determines the best classifier to use as a baseline. We theoretically derive the DD baseline for many commonly used evaluation measures and show that in most situations it reduces to (almost) always predicting either zero or one. Summarizing, the DD baseline is: (1) general, as it is applicable to all binary classification problems; (2) simple, as it is quickly determined without training or parameter-tuning; (3) informative, as insightful conclusions can be drawn from the results. The DD baseline serves two purposes. First, to enable comparisons across research papers by this robust and universal baseline. Secondly, to provide a sanity check during the development process of a prediction model. It is a major warning sign when a model is outperformed by the DD baseline.  ( 2 min )

We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning problems whose state-action space is endowed with a metric. We introduce Kernel-UCBVI, a model-based optimistic algorithm that leverages the smoothness of the MDP and a non-parametric kernel estimator of the rewards and transitions to efficiently balance exploration and exploitation. For problems with $K$ episodes and horizon $H$, we provide a regret bound of $\widetilde{O}\left( H^3 K^{\frac{2d}{2d+1}}\right)$, where $d$ is the covering dimension of the joint state-action space. This is the first regret bound for kernel-based RL using smoothing kernels, which requires very weak assumptions on the MDP and has been previously applied to a wide range of tasks. We empirically validate our approach in continuous MDPs with sparse rewards.  ( 2 min )

In this paper, we develop a new multi-singer Chinese neural singing voice synthesis (SVS) system named WeSinger. To improve the accuracy and naturalness of synthesized singing voice, we design several specifical modules and techniques: 1) A deep bi-directional LSTM based duration model with multi-scale rhythm loss and post-processing step; 2) A Transformer-alike acoustic model with progressive pitch-weighted decoder loss; 3) a 24 kHz pitch-aware LPCNet neural vocoder to produce high-quality singing waveforms; 4) A novel data augmentation method with multi-singer pre-training for stronger robustness and naturalness. Both quantitative and qualitative evaluation results demonstrate the effectiveness of WeSinger in terms of accuracy and naturalness, and WeSinger achieves state-of-the-art performance on the public corpus Opencpop. Some synthesized singing samples are available online (https://zzw922cn.github.io/WeSinger/).  ( 2 min )

In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which were proposed by Sell et al. [39]. Such priors were developed as more robust alternatives to classical architectures in the context of inference problems. For this work we develop multilevel Monte Carlo (MLMC) methods for such models. MLMC is a popular variance reduction technique, with particular applications in Bayesian statistics and uncertainty quantification. We show how a particular advanced MLMC method that was introduced in [4] can be applied to Bayesian inference from DNNs and establish mathematically, that the computational cost to achieve a particular mean square error, associated to posterior expectation computation, can be reduced by several orders, versus more conventional techniques. To verify such results we provide numerous numerical experiments on model problems arising in machine learning. These include Bayesian regression, as well as Bayesian classification and reinforcement learning.  ( 2 min )

In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in non-stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a metric. Using a non-parametric model of the MDP built with time-dependent kernels, we prove a regret bound that scales with the covering dimension of the state-action space and the total variation of the MDP with time, which quantifies its level of non-stationarity. Our method generalizes previous approaches based on sliding windows and exponential discounting used to handle changing environments. We further propose a practical implementation of KeRNS, we analyze its regret and validate it experimentally.  ( 2 min )

When trained on diverse labeled data, machine learning models have proven themselves to be a powerful tool in all facets of society. However, due to budget limitations, deliberate or non-deliberate censorship, and other problems during data collection and curation, the labeled training set might exhibit a systematic shortage of data for certain groups. We investigate a scenario in which the absence of certain data is linked to the second level of a two-level hierarchy in the data. Inspired by the idea of protected groups from algorithmic fairness, we refer to the partitions carved by this second level as "subgroups"; we refer to combinations of subgroups and classes, or leaves of the hierarchy, as "sources". To characterize the problem, we introduce the concept of classes with incomplete subgroup support. The representational bias in the training set can give rise to spurious correlations between the classes and the subgroups which render standard classification models ungeneralizable to unseen sources. To overcome this bias, we make use of an additional, diverse but unlabeled dataset, called the "deployment set", to learn a representation that is invariant to subgroup. This is done by adversarially matching the support of the training and deployment sets in representation space. In order to learn the desired invariance, it is paramount that the sets of samples observed by the discriminator are balanced by class; this is easily achieved for the training set, but requires using semi-supervised clustering for the deployment set. We demonstrate the effectiveness of our method with experiments on several datasets and variants of the problem.  ( 2 min )

We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies. The datasets contain hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories. While public datasets from multiple fundamental physics disciplines already exist, the common interface and provided reference models simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. We discuss the design and structure and line out how additional datasets can be submitted for inclusion. As showcase application, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks. We show that our approach reaches performance close to dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.  ( 2 min )

In real-world applications, it is important and desirable to learn a model that performs well on out-of-distribution (OOD) data. Recently, causality has become a powerful tool to tackle the OOD generalization problem, with the idea resting on the causal mechanism that is invariant across domains of interest. To leverage the generally unknown causal mechanism, existing works assume a linear form of causal feature or require sufficiently many and diverse training domains, which are usually restrictive in practice. In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature. Our approach is based on transformations that modify the non-causal feature but leave the causal part unchanged, which can be either obtained from prior knowledge or learned from the training data in the multi-domain scenario. Under the setting of invariant causal mechanism, we theoretically show that if all such transformations are available, then we can learn a minimax optimal model across the domains using only single domain data. Noticing that knowing a complete set of these causal invariant transformations may be impractical, we further show that it suffices to know only a subset of these transformations. Based on the theoretical findings, a regularized training procedure is proposed to improve the OOD generalization capability. Extensive experimental results on both synthetic and real datasets verify the effectiveness of the proposed algorithm, even with only a few causal invariant transformations.  ( 2 min )

The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from data for explicitly parameterized models, which however are not appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we propose the first machine unlearning algorithm for MCMC. We first convert the MCMC unlearning problem into an explicit optimization problem. Based on this problem conversion, an {\it MCMC influence function} is designed to provably characterize the learned knowledge from data, which then delivers the MCMC unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not compromise the generalizability of the MCMC models. Experiments on Gaussian mixture models and Bayesian neural networks confirm the effectiveness of the proposed algorithm. The code is available at \url{https://github.com/fshp971/mcmc-unlearning}.  ( 2 min )

The problem of Reinforcement Learning (RL) in an unknown nonlinear dynamical system is equivalent to the search for an optimal feedback law utilizing the simulations/ rollouts of the dynamical system. Most RL techniques search over a complex global nonlinear feedback parametrization making them suffer from high training times as well as variance. Instead, we advocate searching over a local feedback representation consisting of an open-loop sequence, and an associated optimal linear feedback law completely determined by the open-loop. We show that this alternate approach results in highly efficient training, the answers obtained are repeatable and hence reliable, and the resulting closed performance is superior to global state-of-the-art RL techniques. Finally, if we replan, whenever required, which is feasible due to the fast and reliable local solution, it allows us to recover global optimality of the resulting feedback law.  ( 2 min )

Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy. In this paper, we show how this robustness arises from hedging against worst-case perturbations of the reward function, which are chosen from a limited set by an imagined adversary. Using convex duality, we characterize this robust set of adversarial reward perturbations under KL and alpha-divergence regularization, which includes Shannon and Tsallis entropy regularization as special cases. Importantly, generalization guarantees can be given within this robust set. We provide detailed discussion of the worst-case reward perturbations, and present intuitive empirical examples to illustrate this robustness and its relationship with generalization. Finally, we discuss how our analysis complements and extends previous results on adversarial reward robustness and path consistency optimality conditions.  ( 2 min )

Non-convex loss functions arise frequently in modern machine learning, and for the theoretical analysis of stochastic optimization methods, the presence of non-isolated minima presents a unique challenge that has remained under-explored. In this paper, we study the local convergence of the stochastic gradient descent method to non-isolated global minima. Under mild assumptions, we estimate the probability for the iterations to stay near the minima by adopting the notion of stochastic stability. After establishing such stability, we present the lower bound complexity in terms of various error criteria for a given error tolerance $\epsilon$ and a failure probability $\gamma$.  ( 2 min )

Fairness emerged as an important requirement to guarantee that Machine Learning (ML) predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey that illustrates the subtleties between fairness notions through a large number of examples and scenarios. In addition, unlike other surveys in the literature, it addresses the question of: which notion of fairness is most suited to a given real-world scenario and why? Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policymakers to navigate the relatively large catalog of ML.  ( 2 min )

We study a finite-horizon restless multi-armed bandit problem with multiple actions, dubbed R(MA)^2B. The state of each arm evolves according to a controlled Markov decision process (MDP), and the reward of pulling an arm depends on both the current state of the corresponding MDP and the action taken. The goal is to sequentially choose actions for arms so as to maximize the expected value of the cumulative rewards collected. Since finding the optimal policy is typically intractable, we propose a computationally appealing index policy which we call Occupancy-Measured-Reward Index Policy. Our policy is well-defined even if the underlying MDPs are not indexable. We prove that it is asymptotically optimal when the activation budget and number of arms are scaled up, while keeping their ratio as a constant. For the case when the system parameters are unknown, we develop a learning algorithm. Our learning algorithm uses the principle of optimism in the face of uncertainty and further uses a generative model in order to fully exploit the structure of Occupancy-Measured-Reward Index Policy. We call it the R(MA)^2B-UCB algorithm. As compared with the existing algorithms, R(MA)^2B-UCB performs close to an offline optimum policy, and also achieves a sub-linear regret with a low computational complexity. Experimental results show that R(MA)^2B-UCB outperforms the existing algorithms in both regret and run time.  ( 2 min )

Since the inception of crowdsourcing, aggregation has been a common strategy for dealing with unreliable data. Aggregate ratings are more reliable than individual ones. However, many natural language processing (NLP) applications that rely on aggregate ratings only report the reliability of individual ratings, which is the incorrect unit of analysis. In these instances, the data reliability is under-reported, and a proposed k-rater reliability (kRR) should be used as the correct data reliability for aggregated datasets. It is a multi-rater generalization of inter-rater reliability (IRR). We conducted two replications of the WordSim-353 benchmark, and present empirical, analytical, and bootstrap-based methods for computing kRR on WordSim-353. These methods produce very similar results. We hope this discussion will nudge researchers to report kRR in addition to IRR.  ( 2 min )

The Kullback-Leibler divergence or relative entropy is an information-theoretic measure between statistical models that play an important role in measuring a distance between random variables. In the study of complex systems, random fields are mathematical structures that models the interaction between these variables by means of an inverse temperature parameter, responsible for controlling the spatial dependence structure along the field. In this paper, we derive closed-form expressions for the Kullback-Leibler divergence between two pairwise isotropic Gaussian-Markov random fields in both univariate and multivariate cases. The proposed equation allows the development of novel similarity measures in image processing and machine learning applications, such as image denoising and unsupervised metric learning.  ( 2 min )

We consider the problem of learning the dynamics of autonomous linear systems (i.e., systems that are not affected by external control inputs) from observations of multiple trajectories of those systems, with finite sample guarantees. Existing results on learning rate and consistency of autonomous linear system identification rely on observations of steady state behaviors from a single long trajectory, and are not applicable to unstable systems. In contrast, we consider the scenario of learning system dynamics based on multiple short trajectories, where there are no easily observed steady state behaviors. We provide a finite sample analysis, which shows that the dynamics can be learned at a rate $\mathcal{O}(\frac{1}{\sqrt{N}})$ for both stable and unstable systems, where $N$ is the number of trajectories, when the initial state of the system has zero mean (which is a common assumption in the existing literature). We further generalize our result to the case where the initial state has non-zero mean. We show that one can adjust the length of the trajectories to achieve a learning rate of $\mathcal{O}(\sqrt{\frac{\log{N}}{N})}$ for strictly stable systems and a learning rate of $\mathcal{O}(\frac{(\log{N})^d}{\sqrt{N}})$ for marginally stable systems, where $d$ is some constant.  ( 2 min )

Transformer-based language models (TLMs) provide state-of-the-art performance in many modern natural language processing applications. TLM training is conducted in two phases. First, the model is pre-trained over large volumes of text to minimize a generic objective function, such as the Masked Language Model (MLM). Second, the model is fine-tuned in specific downstream tasks. Pre-training requires large volumes of data and high computational resources, while introducing many still unresolved design choices. For instance, selecting hyperparameters for language model pre-training is often carried out based on heuristics or grid-based searches. In this work, we propose a multi-armed bandit-based online optimization framework for the sequential selection of pre-training hyperparameters to optimize language model performance. We pose the pre-training procedure as a sequential decision-making task, where at each pre-training step, an agent must determine what hyperparameters to use towards optimizing the pre-training objective. We propose a Thompson sampling bandit algorithm, based on a surrogate Gaussian process reward model of the MLM pre-training objective, for its sequential minimization. We empirically show how the proposed Gaussian process based Thompson sampling pre-trains robust and well-performing language models. Namely, by sequentially selecting masking hyperparameters of the TLM, we achieve satisfactory performance in less epochs, not only in terms of the pre-training MLM objective, but in diverse downstream fine-tuning tasks. The proposed bandit-based technique provides an automated hyperparameter selection method for pre-training TLMs of interest to practitioners. In addition, our results indicate that, instead of MLM pre-training with fixed masking probabilities, sequentially adapting the masking hyperparameters improves both pre-training loss and downstream task metrics.  ( 2 min )

Deep neural networks (DNNs) have been successfully applied to many real-world problems, but a complete understanding of their dynamical and computational principles is still lacking. Conventional theoretical frameworks for analysing DNNs often assume random networks with coupling weights obeying Gaussian statistics. However, non-Gaussian, heavy-tailed coupling is a ubiquitous phenomenon in DNNs. Here, by weaving together theories of heavy-tailed random matrices and non-equilibrium statistical physics, we develop a new type of mean field theory for DNNs which predicts that heavy-tailed weights enable the emergence of an extended critical regime without fine-tuning parameters. In this extended critical regime, DNNs exhibit rich and complex propagation dynamics across layers. We further elucidate that the extended criticality endows DNNs with profound computational advantages: balancing the contraction as well as expansion of internal neural representations and speeding up training processes, hence providing a theoretical guide for the design of efficient neural architectures.  ( 2 min )

Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. Several variants of CCA have been introduced in the literature, in particular, variants based on deep neural networks for learning highly correlated nonlinear transformations of two views. As these models are parameterized conventionally, their learnable parameters remain independent of the inputs after the training process, which may limit their capacity for learning highly correlated representations. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model. In our deep-CCA models, the parameters of the last layer are scaled by a second neural network that is conditioned on the model's input, resulting in a parameterization that is dependent on the input samples. We evaluate our model on multiple datasets and demonstrate that the learned representations are more correlated in comparison to the conventionally-parameterized CCA-based models and also obtain preferable retrieval results. Our code is available at https://github.com/tomerfr/DynamicallyScaledDeepCCA.  ( 2 min )

Although Shapley Values (SV) are widely used in explainable AI, they can be poorly understood and estimated, implying that their analysis may lead to spurious inferences and explanations. As a starting point, we remind an invariance principle for SV and derive the correct approach for computing the SV of categorical variables that are particularly sensitive to the encoding used. In the case of tree-based models, we introduce two estimators of Shapley Values that exploit the tree structure efficiently and are more accurate than state-of-the-art methods. Simulations and comparisons are performed with state-of-the-art algorithms and show the practical gain of our approach. Finally, we discuss the ability of SV to provide reliable local explanations. We also provide a Python package that computes our estimators at https://github.com/salimamoukou/acv00.  ( 2 min )

Instrumental variables regression is a popular causal inference method for endogenous treatment. A significant concern in practical applications is the validity and strength of instrumental variables. This paper aims to perform causal inference when all instruments are possibly invalid. To do this, we propose a novel methodology called two stage curvature identification (TSCI) together with a generalized concept to measure the strengths of possibly invalid instruments: such invalid instruments can still be used for inference in our framework. We fit the treatment model with a general machine learning method and propose a novel bias correction method to remove the overfitting bias from machine learning methods. Among a collection of spaces of violation functions, we choose the best one by evaluating invalid instrumental variables' strength. We demonstrate our proposed TSCI methodology in a large-scale simulation study and revisit the important economics question on the effect of education on earnings.  ( 2 min )

Current domain adaptation methods address the problems of covariate shift or label shift, but are not applicable to the setting where they occur simultaneously and interact with each other. In this paper, we propose an assumption, confounded shift, to begin to address this problem. We also propose a framework for this task, based on minimizing the expected divergence between the source and target conditional distributions. Within this framework, we propose using the reverse KL divergence, demonstrating the use of both parametric linear Gaussian and nonparametric nonlinear Gaussian Process estimators of the conditional distribution. We also propose using the Maximum Mean Discrepancy (MMD) within our framework. To make confounded domain adaptation with the MMD effective, we propose an intelligent dynamic strategy for choosing the kernel bandwidth, which may be of independent interest even outside of the confounded shift context. Finally, we show that our approach is advantageous on a variety of synthetic and real datasets.  ( 2 min )

Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations. There has been much work on improving generalization of DML in settings like zero-shot retrieval, but little is known about its implications for fairness. In this paper, we are the first to evaluate state-of-the-art DML methods trained on imbalanced data, and to show the negative impact these representations have on minority subgroup performance when used for downstream tasks. In this work, we first define fairness in DML through an analysis of three properties of the representation space -- inter-class alignment, intra-class alignment, and uniformity -- and propose finDML, the fairness in non-balanced DML benchmark to characterize representation fairness. Utilizing finDML, we find bias in DML representations to propagate to common downstream classification tasks. Surprisingly, this bias is propagated even when training data in the downstream task is re-balanced. To address this problem, we present Partial Attribute De-correlation (PARADE) to de-correlate feature representations from sensitive attributes and reduce performance gaps between subgroups in both embedding space and downstream metrics.  ( 2 min )

B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations.  ( 2 min )

Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of every high-level action in existence, but instead only consider ones they deem possible in the first place. By focusing only on what is feasible, or "afforded", at the present moment, an agent can spend more time both evaluating the utility of and acting on what matters. To this end, we present Hierarchical Affordance Learning (HAL), a method that learns a model of hierarchical affordances in order to prune impossible subtasks for more effective learning. Existing works in hierarchical reinforcement learning provide agents with structural representations of subtasks but are not affordance-aware, and by grounding our definition of hierarchical affordances in the present state, our approach is more flexible than the multitude of approaches that ground their subtask dependencies in a symbolic history. While these logic-based methods often require complete knowledge of the subtask hierarchy, our approach is able to utilize incomplete and varying symbolic specifications. Furthermore, we demonstrate that relative to non-affordance-aware methods, HAL agents are better able to efficiently learn complex tasks, navigate environment stochasticity, and acquire diverse skills in the absence of extrinsic supervision -- all of which are hallmarks of human learning.  ( 2 min )

Bayesian optimization is a gold standard for query-efficient continuous optimization. However, its adoption for drug and antibody sequence design has been hindered by the discrete, high-dimensional nature of the decision variables. We develop a new approach (LaMBO) which jointly trains a denoising autoencoder with a discriminative multi-task Gaussian process head, enabling gradient-based optimization of multi-objective acquisition functions in the latent space of the autoencoder. These acquisition functions allow LaMBO to balance the explore-exploit trade-off over multiple design rounds, and to balance objective tradeoffs by optimizing sequences at many different points on the Pareto frontier. We evaluate LaMBO on a small-molecule task based on the ZINC dataset and introduce a new large-molecule task targeting fluorescent proteins. In our experiments, LaMBO outperforms genetic optimizers and does not require a large pretraining corpus, demonstrating that Bayesian optimization is practical and effective for biological sequence design.  ( 2 min )

Gaming, creating CGI movies, building shared worlds, and creating digital twins are exciting in principle, but the complexity of building 3D models usually serves to limit the ambition of even the most dedicated auteur. However, recent innovations by nVidia, announced earlier this year for their RTX 3090 line of GPUs, are very likely to change… Read More »NERFs To Make 3D As Simple As Shooting a Video The post NERFs To Make 3D As Simple As Shooting a Video appeared first on Data Science Central.  ( 3 min )

Hey r/machinelearning! Recently when working on a WorldModels implementation for keras.io I realized that I needed a genetic algorithm implementation to train the "controller" module. Instead of writing a one off solution, I decided to write Keras Genetic, a full package to train keras models using genetic algorithms. Please note genetic algorithms are not good for training neural networks outside of some niche use cases; typically training a controller with <1k parameters. The ConvNet MNIST example scores *horribly* when compared to comparable backprop examples. Please give the package a try and let me know if you find this interesting or useful: https://github.com/lukewood/keras-genetic submitted by /u/puppet_pals [link] [comments]  ( 1 min )

Hello, For a personal project, I intend to develop a little CNN - TransposeCNN model to colorize images of portraits. The idea is simply, from a grayscale image, to give the RGB version. To do so, I constructed a dataset of colored portraits and I would like to use the keras' ImageDataGenerator tool to specify that the feature to be the grayscale version and the label the original one. I could simply duplicate the current dataset and convert one of them into grayscales, and that may be easier to do, but then I would like to do something else : I would like to apply the same data augmentation functions from ImageDataGenerator (rotations, flipping... ) to the features and their corresponding labels. Do you know if it is possible or will I have to construct the augmented dataset explicitely ? Thank you for your advice submitted by /u/Arioxel_ [link] [comments]  ( 1 min )

According to the original "Attention is all you need" paper (Section 6), for the base models, they used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. Is it about improving the performance? But if other works didn't do the checkpoint averaging, it wouldn't be a fair comparision. However, I seldom see the recently transformer works highlighting this technique. What's more, I have not heard any ViT (Vision Transformer) works utilizing such a checkpoint averaging trick. My background is computer vision so I was wondering if it makes sense to try this... Could someone provide some guidance on this? Thanks. submitted by /u/AaronSpalding [link] [comments]  ( 2 min )

Currently, for a sequence classification problem I'm using a pretrained network (Transformer or RNN) as a feature extractor. I average across time to obtain a d-dimensional vector per training example and train classifier on top of these feature vectors. I see two improvements to simple averaging of the features: Add an average pooling layer and train the network end-to-end. Add a weighted average pooling layer and train the network end-to-end Are there any ways that I can do better here? I am under sample size limitations, samples per class can be as low as 20, total data set size under 300, with 2-3 classes. I've tried method 1. and it barely changes the classification accuracy. submitted by /u/PK_thundr [link] [comments]  ( 1 min )

Are there any standard benchmarking tools or charts which compares CPU's to compare how much faster one performs than the other relative to ML training/predicting? For example, a "5600X" will train models 30% faster than a "2600X, etc. which may be seconds saved on a small model our hours on a larger model. In the example above, I've actually a 2600X for the last 2 years and am considering to buy a 5600X, wait until AM5 CPU later this year, or switch back to Intel 12600K although Intel is at least $200 more because their MB are overpriced. It would be nice to know whether it would save me incredible amount of time so I can justify getting more experience quicker or if it's marginal and to save the money on a nice 3080 GPU, etc. and get into Deep Learning sooner. submitted by /u/bugsysiegals [link] [comments]  ( 1 min )

https://youtu.be/_EDr3ryrT_Y Modern language models like T5 or GPT-3 achieve remarkably low perplexities on both training and validation data, yet when sampling from their output distributions, the generated text often seems dull and uninteresting. Various workarounds have been proposed, such as top-k sampling and nucleus sampling, but while these manage to somewhat improve the generated samples, they are hacky and unfounded. This paper introduces typical sampling, a new decoding method that is principled, effective, and can be implemented efficiently. Typical sampling turns away from sampling purely based on likelihood and explicitly finds a trade-off between generating high-probability samples and generating high-information samples. The paper connects typical sampling to psycholinguistic theories on human speech generation, and shows experimentally that typical sampling achieves much more diverse and interesting results than any of the current methods. ​ OUTLINE: 0:00 - Intro 1:50 - Sponsor: Fully Connected by Weights & Biases 4:10 - Paper Overview 7:40 - What's the problem with sampling? 11:45 - Beam Search: The good and the bad 14:10 - Top-k and Nucleus Sampling 16:20 - Why the most likely things might not be the best 21:30 - The expected information content of the next word 25:00 - How to trade off information and likelihood 31:25 - Connections to information theory and psycholinguistics 36:40 - Introducing Typical Sampling 43:00 - Experimental Evaluation 44:40 - My thoughts on this paper ​ Paper: https://arxiv.org/abs/2202.00666 Code: https://github.com/cimeister/typical-sampling submitted by /u/ykilcher [link] [comments]  ( 1 min )

One core idea in ML is to use/build a simple model at first to get some minimum threshold, and then find better models or refine the existing model, improve the data, etc. I'd say for computer vision, in most cases, a Resnet / EfficientNet would get a good result, given enough data. In NLP, if it's about something very simple, Naive Bayes methods can be decent. If the task is harder, BERT would do the trick up to a certain level for many tasks However, choosing the first model is not obvious for many graph-related tasks where a node has more than 1 feature. For example, in node classification problems, what's a model easy to implement that guarantees decent results? submitted by /u/adenml [link] [comments]  ( 1 min )

For example, when is it better to use decision trees instead of SVM or KNN, based on underlying theory/distribution of the data? I would appreciate any empirical/theoritical advice or references. Thank you. submitted by /u/Redditagonist [link] [comments]  ( 3 min )

TorchMetrics is a really nice and convenient library that lets us compute the performance of models in an iterative fashion. It's designed with PyTorch (and PyTorch Lightning) in mind, but it is a general-purpose library compatible with other libraries and workflows. This iterative computation is useful if we want to track a model during iterative training or evaluation on minibatches (and optionally across on multiple GPUs). In deep learning, that's essentially all the time. However, when using TorchMetrics, one common question is whether we should use .update() or .forward()? (And that's also a question I certainly had when I started using it.). Here's a hands-on example and explanation. https://sebastianraschka.com/blog/2022/torchmetrics.html submitted by /u/seraschka [link] [comments]  ( 1 min )

I came across this Twitter thread which shows some interesting results when trying to recolor historical photos that have been decolored. The recolored photos are a lot drabber than the original, and the thread author says that this gives us a skewed view of the past, making us think the past was a lot more boring than it was, downplaying how vibrant and diverse certain societies were. https://preview.redd.it/fctveek4cjp81.png?width=3064&format=png&auto=webp&s=9aadecef7c12a4dd13f5197a69ffd155084be75e This made me think of some questions that I thought could lead to a good discussion here. I've put some below in no particular order! Do you think that expert opinion should be consulted in the Machine Learning process more? If so, where? (perhaps omitting Expert Systems) Is there too much faith that a result from an ML model is the "right" result? (a phenomenon that maybe isn't specific to ML but a result of human tendencies?) Do ML practitioners have a responsibility to clearly communicate to the general public the limitations and degree-of-confidence in these systems? Am I reading too much into this, and this colorization model is just a fun model to play with, and the conclusions of the Twitter thread are too speculative or conjectural? Is this colorization issue just another form of bias that needs to be ironed out? The thread concludes by saying that colorization should be left to experts who can use context to pick accurate colors. I think this is too extreme, and that ML systems can incorporate expertise when training, or after during evaluation. Do you think there are any jobs/problems that ML methods could be applied to but should be left to experts (some considerations might be safety, privacy, ethics, etc.) I know that ultimately a lot of these questions can simply boil down to statistics and their interpretation, so I'm not sure exactly where discussion could/should/will lead, but I'm looking forward to hearing your opinions :) submitted by /u/SleekEagle [link] [comments]  ( 7 min )

Hi, I am wondering if there are any ways to test large neural networks (to see if they work) before training the entire model? In any case, I don't really want to train the model (which would take a long time for me) and find out that it does not converge or converges poorly. submitted by /u/ChunkOfAir [link] [comments]

https://youtu.be/Z3knUzwuIgo This is an interview with Junnan Li and Dongxu Li, authors of BLIP and members of Salesforce research. Cross-modal pre-training has been all the rage lately in deep learning, especially training vision and language models together. However, there are a number of issues, such as low quality datasets that limit the performance of any model trained on it, and also the fact that pure contrastive pre-training cannot be easily fine-tuned for most downstream tasks. BLIP unifies different tasks and objectives in a single pre-training run and achieves a much more versatile model, which the paper immediately uses to create, filter, clean and thus bootstrap its own dataset to improve performance even more! ​ OUTLINE: 0:00 - Intro 0:40 - Sponsor: Assembly AI 1:30 - Start of Interview 2:30 - What's the pitch? 4:40 - How did data bootstrapping come into the project? 7:10 - How big of a problem is data quality? 11:10 - Are the captioning & filtering models biased towards COCO data? 14:40 - Could the data bootstrapping be done multiple times? 16:20 - What was the evolution of the BLIP architecture? 21:15 - Are there additional benefits to adding language modelling? 23:50 - Can we imagine a modular future for pre-training? 29:45 - Diving into the experimental results 42:40 - What did and did not work out during the research? 45:00 - How is research life at Salesforce? 46:45 - Where do we go from here? ​ Paper: https://arxiv.org/abs/2201.12086 Code: https://github.com/salesforce/BLIP Demo: https://huggingface.co/spaces/Salesforce/BLIP submitted by /u/ykilcher [link] [comments]  ( 1 min )

This post is co-written by Shibangi Saha, Data Scientist, and Graciela Kravtzov, Co-Founder and CTO, of Equilibrium Point. Many individuals are experiencing new symptoms of mental illness, such as stress, anxiety, depression, substance use, and post-traumatic stress disorder (PTSD). According to Kaiser Family Foundation, about half of adults (47%) nationwide have reported negative mental health […]  ( 8 min )

Amazon Kendra is an intelligent search service powered by machine learning. You can receive spelling suggestions for misspelled terms in your queries by utilizing the Amazon Kendra Spell Checker. Spell Checker helps reduce the frequency of queries returning irrelevant results by providing spelling suggestions for unrecognized terms. In this post, we explore how to use […]  ( 4 min )

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Graph Neural Networks (GNNs) have proved to be effective in a wide range of issues and fields. GNNs commonly use a message-passing mechanism, in which nodes communicate feature representations (“messages”) to their neighbors at each layer. Each node’s feature representation is initialized to its original features, and it is updated by aggregating incoming messages from neighbors on a regular basis. GNNs are distinguished from other purely topological learning systems such as random walks or label propagation by their ability to mix topological and feature information, which is arguably what contributes to their success. Typically, GNN models assume a fully observed feature matrix, with rows representing nodes and columns representing channels. In real-world circumstances, however, each trait is frequently only observable for a subset of nodes. Demographic information, for example, maybe exposed to only a small percentage of social network users, while content features are typically only available to the most active users. Continue Reading Paper: https://arxiv.org/pdf/2111.12128.pdf https://preview.redd.it/nylt2m19fkp81.png?width=1024&format=png&auto=webp&s=639c61207d4bffaa4c67f97263fcd5527a849f85 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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I dont really know how a i is done i imagine with lots of code i dont know and loads of statistics i really dont know. Can someone out in the land of 1's aand 0's point me in the right direction? submitted by /u/143openyourmind [link] [comments]  ( 1 min )

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The amount of money and energy necessary to train AI models has become a hot-button issue as they grow in size. Leaders in the AI field have been pouring money towards training increasingly bigger models since GPT-3 proved the considerable gains in performance that can be achieved by merely increasing model size. However, this is prohibitively expensive, necessitates tremendous computational resources, consumes enormous energy, and is becoming more recognized as an issue, not just because of the environmental implications, but also because it makes it harder for smaller AI companies to compete, concentrating power in the hands of industry titans. A new technique that rewrites one of the discipline’s core building pieces could give a workaround. Oxford University researchers have proposed a novel method that might cut training time in half. This is accomplished by redesigning backpropagation, one of the essential components of today’s neural network-based AI systems. Backpropagation has remained a mainstay of machine learning for computing gradients of objective functions for optimization. Backpropagation, also known as reverse-mode differentiation, is a subset of the general family of automatic differentiation algorithms that includes forward mode differentiation. A method for computing gradients based purely on the directional derivative, which may be done in the forward mode with precision and efficiency, is designed. The method is known as the forward gradient, which is an unbiased estimate of the gradient that can be assessed in a single forward run of the function, obviating the necessity for backpropagation in gradient descent completely. Continue Reading The Research Summary Paper: https://arxiv.org/pdf/2202.08587.pdf submitted by /u/No_Coffee_4638 [link] [comments]  ( 2 min )

Hi! I hope this is the right subreddit... I am currently studying Electrical Engineering at University and studying for an exam that is (partly) about Neural NEtworks. I am struggling a bit with an example given about calculating the entropy of a decision tree and hope someone here can help me out. I have the following information: Given information The table is the given dataset and the tree is the resulting tree. I am trying to understand how the calculated Entropy values came to place, since I get different answers when I try it myself. This is my way (for example for "level"): p1 (Senior) = 5/14 p2 (Mid) = 4/14 p3 (Junior) = 5/14 Entropy = -(5/14*log_2(5/14)+4/14*log_2(4/14)+5/14*log_2(5/14)) = 1.577 (which makes no sense, since Entropy should be between 0 and 1??) Thanks a lot for any help! submitted by /u/inc0mingst0rm [link] [comments]  ( 1 min )

I've been trying to program a "game" where drones collect points floating around using a neural network with NEAT. I've tried tinkering a bit with the config file, but the drones just don't seem to evolve... https://stackoverflow.com/questions/71611034/python-neat-neural-network-not-evolving I guess i just want to know what i'm doing wrong, since nothing appears to be "evolving" when i run the program... (NEAT v0.92 and python v3.9.2 btw) link to the github repo is here aswell submitted by /u/LukasSchulte [link] [comments]  ( 1 min )

There is a Tensorflow function that I cannot convert into PyTorch. Can I use that function from tensorflow but still use my entire architecture in PyTorch? submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

To clarify, deep q networks and double q networks would it ever discover such actions if its always favouring higher goals in the short term? what if another action has to be performed that may give a loss (potentially a significant loss) in the short term but in fact sets a path for a greater reward? are there any papers I could look at? submitted by /u/clockface99 [link] [comments]  ( 3 min )

I am new to RL but have experience on Deep Learning. Would you please guide me in the right direction as to how can I apply Deep Reinforcement Learning in Arcade Learning Environment? I also have basic knowledge of OpenAI gym environment. submitted by /u/AvailableBike9260 [link] [comments]  ( 1 min )

Correct me if I am wrong. Mathematically, Linear Approximation L = f(X_0) + ∇f(X_0)(X-X_0) and Quadratic Approximation Q = f(X_0) + ∇f(X_0)(X-X_0) + 0.5 * (X-X_0)^T H_f(X_0) (X-X_0). Then in TRPO algorithm, why are we ignoring the constant term f(X_0) for importance sampling term and f(X_0) + ∇f(X_0)(X-X_0) for Quadratic Approximation in KL Term. submitted by /u/Better-Ad8608 [link] [comments]  ( 1 min )

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If you want to ride the next big wave in AI, grab a transformer. They’re not the shape-shifting toy robots on TV or the trash-can-sized tubs on telephone poles. So, What’s a Transformer Model? A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the Read article > The post What Is a Transformer Model? appeared first on NVIDIA Blog.  ( 9 min )

When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Known as inverse Read article > The post NVIDIA Research Turns 2D Photos Into 3D Scenes in the Blink of an AI appeared first on NVIDIA Blog.  ( 4 min )

To learn visual representations, self-attention-based models, particularly vision transformers, offer an alternative to convolutional neural networks (CNNs). ViT breaks an image into a series of non-overlapping patches and then uses multi-headed self-attention in transformers to learn interpatch representations. The typical trend in ViT networks is to increase the number of parameters to improve performance. These gains in performance come at the expense of model size (network parameters) and latency. Many real-world applications (such as augmented reality and autonomous wheelchairs) necessitate the timely execution of visual recognition tasks (such as object detection and semantic segmentation) on resource-constrained mobile devices. ViT models for such jobs should be light-weight and quick to be effective. ViT models’ performance is much inferior to light-weight CNNs, even when the model size is reduced to fit the resource limits of mobile devices. DeIT, for example, is 3% less accurate than MobileNetv3 for a parameter budget of roughly 5-6 million. As a result, designing light-weight ViT models is critical. Many mobile vision tasks have been powered by light-weight CNNs. ViT-based networks, on the other hand, are still a long way from being utilized on such devices. Unlike light-weight CNNs that are simple to optimize and integrate with task-specific networks, ViTs are large (e.g., ViT-B/16 vs. MobileNetv3: 86 vs. 7.5 million parameters), difficult to optimize, require extensive data augmentation and L2 regularisation to avoid over-fitting, and require expensive decoders for downstream tasks, particularly dense prediction tasks. Continue Reading The Research Summary Paper: https://arxiv.org/pdf/2110.02178.pdf Github: https://github.com/apple/ml-cvnets https://preview.redd.it/u1m6udvqyep81.png?width=1198&format=png&auto=webp&s=b12153662902056706dd2ff92459c625233bb785 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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I did a quick search of huggingface but couldn't find anything. Has anyone tried making a transformer-based reply bot or chatbot that responds to both images and text, like how a person would be able to chat in messaging apps? I thought about this because I figured tweet generation models would be much more interesting if they could also generate replies instead of standalone tweets, and replies by tweeters more often than not are replies to tweets with images. I was thinking not only do you need an image-to-text model, you probably also need finetuning data that includes an image-to-text model? I guess understanding context would be major obstacle. Thanks! submitted by /u/IndicatorGlobe679 [link] [comments]  ( 1 min )

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I'm a soon-to-be master's graduate in artificial intelligence and I'm looking for some career advice. I most likely want to go for a PhD because I enjoy thinking about challenging problems and trying out new things. My dream job would be to work as a research scientist in the industry, which would guarantee a good salary (as opposed to academia), applied value in the research I do, and some creativity. Currently, I am considering different research avenues while keeping in mind a potential trade-off between utility, potential to be creative, intrinsic motivation, and future job prospects. While intrinsic motivation is definitely important to me, I really see a PhD as an investment in my future. That is, I want my job prospects to signficantly improvea after completing it. Furthermore, I am…  ( 7 min )

submitted by /u/sivasiriyapureddy [link] [comments]

This is not an attempt to bend any forum rules, I am not looking for someone in this forum to hack for us or anything. We just need advice as to where to look more than the avenues we have taken. TL;DR: Girlfriend is being stalked by some A.I.-type thing that very specifically, and very repeatedly, targets her and reminds her of abuse and we don't know where to turn for more help, or what it could be, and where it is coming from. Now the details: Over the last year or so, her phone was hacked and started acting funny, apps not working right, etc. During the same time, someone she knew got a hold of her bank information and ID. Someone also logged into Github on her phone (we are only familiar with generally what they do or what can be done there). People started following her around in …  ( 3 min )

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​ https://preview.redd.it/7ztp9xcapep81.png?width=1410&format=png&auto=webp&s=370f3d2554e06343996c9e69a223272a25268e16 The following notebook demonstrates a method to provide explainability for semantic search. This method masks/replaces each token for a search result to determine the importance of each term for a query. The score is then compared with the score for the full text to determine how much of a delta the permutation caused. https://colab.research.google.com/github/neuml/txtai/blob/master/examples/32_Model_explainability.ipynb submitted by /u/davidmezzetti [link] [comments]

Hi guys, I hope this is allowed! I'm a PhD researcher at the University of Bristol in the UK, focussing on Human-In-The-Loop AI. I (and the others in my group) work a lot in XAI and tricky, grey-area ethical issues. Currently I'm researching how different groups, including those familiar with AI, view AI in high-stakes settings. As part of this I'm trying to find people to take part in my survey: https://forms.office.com/r/xVFu2f8qrJ It takes about 3m on average to complete, and there's the option to enter a raffle for one of four £25 Amazon vouchers. All of your data is completely anonymous, and the raffle is separated from the questions, so there's no way that I can associate your answers with your email. If you have any questions feel free to drop them in the comments! submitted by /u/Odd_Beautiful2592 [link] [comments]  ( 3 min )

Hello all, An idea for modelling long-range interactions in CNNs came to my mind, and I thought to share it with you folks and seek your opinions. On a high level, it functions akin to squeeze-and-excitation, with the chief difference being instead of calibrating channel-wise features, it operates on spatial features. Concretely, assume we start with a 3 X 256 X 256 tensor; the first step is to squeeze the channels via a convolution to get a 256 X 256 tensor. However, owing to the computational costs of the multilayer perceptron that is about to be described, it is necessary to downscale the spatial dimensions, which can be done by setting the convolution's kernel size and stride to, say, 16 (i.e., the input is being "patchified"). The resulting 16 X 16 matrix is flattened to yield a 25…  ( 2 min )

-Basically I want to learn ML, to apply in scientific computing. -Learn Scientific computing, visualization and Simulations -it will be more helpful if someone knows pathway to computational biology and guide me submitted by /u/Grapes_icecream [link] [comments]

I love R. It's simple in syntax and very easy to install and run with Rstudio. Some of the data transformation libraries are very good. And even analytics libraries provide more statistical insights. Not to say that Python does not edge out at some places. Like having lots of libraries for almost everything, being multi-functional and huge community. But yet, I enjoy R more than Python. I personally find that I can get most of work done quickly in R. Or is it because I've been working with R, that I'm better at it!? submitted by /u/maverick_css [link] [comments]  ( 2 min )

Hi everyone, I am a Researcher at UXP2 Lab Purdue University working on an NSF funded project to support practitioners to improve ethics in their everyday practice. Are a Machine Learning Engineer who likes to think about ethics in your everyday work? I would love for you to attend one of our virtual workshops. This three-hour workshop will engage you in co-design activities with 3-5 other technology practitioners to build, iterate on, and implement ethics-focused action plans based on provided prompts and your personal experiences. A $50 incentive will be provided for your participation. Fill out the following form to express your interest in this study:https://forms.gle/Ac89zyJLdTrnaHVS9 We are looking for practitioners who are currently employed in roles that include (but are not limited to): Software Engineering, Data Science, Front/Back-end Development, Product Management, User Experience (UX), and other design or technology personnel responsible for the development of digital systems in any industry or governmental context. You can learn more about the overall grant project at https://everydayethics.uxp2.com. I am looking forward to seeing you at the virtual workshop. Thank you! If you have any questions, please feel free to message me! submitted by /u/rikeob [link] [comments]  ( 1 min )

A bit of my background: I completed a Ph.D. in statistics in Europe last year. For my Ph.D., I did some work on DL (and also had some papers published). Directly after finishing my Ph.D. I started working as a data scientist in the financial service industry. However, my goal is to move into a pure tech company and hopefully work as a research data scientist or a research scientist. Recently I have been offered a 2-year postdoc in ML in a good group in Europe. I am considering taking this postdoc since I get more time to do "pure" research (and to complete some of the research from my Ph.D.). Thus, would it make sense to take this postdoc with a lower salary (and worse work-life balance) for a few years if the goal is to get a "research" job in the industry, or would I be better of to try to "climb the ladder" from my current data science job? PS: I am new to this subreddit so I hope this post is ok. submitted by /u/Other_Hotel_6099 [link] [comments]  ( 2 min )

Any ideas which GAN he is using in his latest works? Resolution and detail are insane..https://www.instagram.com/ronjafman/ https://preview.redd.it/gxzu3ielddp81.png?width=1164&format=png&auto=webp&s=a0e3ad2d345989524d271a15df62e1d8245ffd7f submitted by /u/janni95 [link] [comments]  ( 1 min )

I have a lot of untagged images, I want to show the data annotator close looking patches together, which they can just select and then give label at once. Steamlit doesn't work because it doesn't allow for batch selection of images. Is there any easy to use framework available in python using which I can make a frontend for data tagging? submitted by /u/lMAObigZEDONG [link] [comments]  ( 1 min )

Hi all, It might be of interest that I created a Python implementation of IncrementalDBSCAN. The repository, including documentation, is here: https://github.com/DataOmbudsman/incdbscan And this is the original paper: https://www.dbs.ifi.lmu.de/Publikationen/Papers/VLDB-98-IncDBSCAN.pdf The paper (from the authors of DBSCAN) describes how to make DBSCAN work with an incremental strategy, in which one can add new data points to an already existing clustering and doesn't have to re-cluster every data point. I couldn't find any implementation of the algorithm so I ended up writing my own one. I'm glad it's actually working and has a lot of unit tests covering common and special cases. The performance gain (vs reclustering with DBSCAN) is not huge though, could be worked on more. Happy to take any feedback! submitted by /u/DataOmbudsman [link] [comments]  ( 2 min )

Hello all, After 1 year working extensively with GPT models (GPT-3, GPT-J, and GPT-NeoX), I think I now have a good view on what these NLP models are capable of. It appears that many traditional NLP tasks can now be achieved thanks to these large language models thanks to few-shot learning (aka "prompting", or "prompt engineering"). NER is a very good candidate because, thanks to these models, it is possible to extract any type of entity without ever annotating and training a new model. Annotation has always been a challenge that has caused many entity extraction projects to simply fail, because it is a long and tedious process. In this article, I'm showing how easy it is to perform NER thanks to GPT and few-shot learning, without any annotation process: https://nlpcloud.io/few-shot-ner-entity-extraction-without-annotation-training-based-on-gpt.html If you also experimented with entity extraction with GPT models, I would love to hear your thoughts. Are you, like I am, impressed by the results? And do you think it means that annotation is a thing from the past? Thanks! submitted by /u/juliensalinas [link] [comments]  ( 1 min )

Lots of options out there. I'm wondering how useful they are and whether they help you build better apps/services? submitted by /u/dmart89 [link] [comments]  ( 6 min )

After reading the post on Is Geometry a Language That Only Humans Know? I see comments about spiders and their fascinating capabilities to use their legs and vibration sensor and not their eyes to build the web. I had an episode about it and one of the questions about the error assessment and how they know when they should stop building or whether it is right? How they can know if there is a damage in the web. Also the interesting part about their brain behavior. Links in the comment in case you are interested. submitted by /u/meldiwin [link] [comments]  ( 1 min )

They introduce a new method to learn simulators from depth and RGB videos. The "URDF" of an articulated rigid-body mechanism is reconstructed, and the parameters of the simulator inferred through Bayesian inference. Website: https://eric-heiden.github.io/video2sim/ Paper: https://arxiv.org/abs/2203.10488 Twitter Summary: https://twitter.com/eric_heiden/status/1506756429285191682 submitted by /u/hardmaru [link] [comments]  ( 1 min )

A general public paper in the New-York Times explores whether humans are the only species able to master geometry. Neuroscientists like Stanislas Dehaene at Collège de France, Moira Dillon at New York University, and Josh Tenenbaum at MIT are exploring whether human ability to reason about geometric shapes like squares and rectangles are part of what makes humans special. Dehaene said. “I love the progress in A.I. It’s very impressive. But I believe that there is a deep aspect missing, which is symbol processing”. Yoshua Bengio, at the Université de Montréal, agreed that state-of-the-art machine learning lacks something related to symbols or abstract reasoning. It’s not impossible to do abstract reasoning with neural networks, Bengio said, “it’s just that we don’t know yet how to do it.” It’s an indication, Bengio said, of where A.I. research needs to go. submitted by /u/ClaudeCoulombe [link] [comments]  ( 4 min )

I work with NLP primarily, and find that sometimes it's faster on one or the other. Surprisingly, evaluation on GPU takes as much time as training, even if I do .eval(). I don't understand why. Is there a general rule of thumb on if evaluation should be done on GPU or CPU? Any tips? Thanks! submitted by /u/cuddle_cuddle [link] [comments]  ( 1 min )

Hi! Newbie here! I am attempting to pre-train a custom policy in stable baselines. The policy takes multiple inputs, each with a different shape. I tried using this as reference https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/pretraining.ipynb#scrollTo=toKEQE9i8aof but failed miserably. Has anyone successfully pretrained a multi input? Would love some guidance. Currently each of my observations are stored as a list of lists with different sizes.. For example [ list of size(7), list of size(3)...] where each list is one of the inputs. Thank you! submitted by /u/Alpha-Seirra [link] [comments]  ( 1 min )

Does it pay off to make an algorithm from scratch if there are libs like ray? I implemented an own distributed algorithm. Anyway i wounder if it has any worth because there are libs like ray where popular algos are already implemented. It even has hyper parameter tuning. What do you guys think? submitted by /u/Willing-Classroom735 [link] [comments]  ( 1 min )

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How does nebullvm work? It takes your AI model as input and outputs an optimized version that runs 5-20 times faster on your hardware. In other words, nebullvm tests multiple deep learning compilers to identify the best possible way to execute your model on your specific machine, without impacting the accuracy of your model. And that's it. In just a few lines of code. And a big thank you to everyone for supporting this open-source project! The library received 250+ Github stars⭐ on release day, and that's just amazing 🚀 ORIENTATION MAP Let's learn more about nebullvm and AI optimization. Where should we start? From... Some CONTEXT on why few developers optimize AI and related negative consequences An overview of how the LIBRARY works Some USE CASES, technology demonstrations and…  ( 6 min )

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Hey , not sure if this is the right place to ask or not, i think itd be. Basically i need to create a neural net from scratch in c# for intent classification. I have modelled the neurons as a class with attributes : weights(moddelled as an array, each value is a weight for a branch connecting to the neuron) a , a value(result after the activation),and a bias(a different bias per neuron. My question is that , my input will be an array of words, obviously hashed or something so it has a numerical value, each value in the array will be multiplied by the weight. Is the bias added to each of these values and then the activation function applied to each value in the array, OR is it that the activation is applied to the array there is one value as an output and the bias is added to that. thank you any help appreciated , if more info is needed then please ask. submitted by /u/Tubhalooter [link] [comments]  ( 1 min )

Posted by Boris Babenko, Software Engineer and Naama Hammel, Clinical Research Scientist, Google Health Three years ago we wrote about our work on predicting a number of cardiovascular risk factors from fundus photos (i.e., photos of the back of the eye)1 using deep learning. That such risk factors could be extracted from fundus photos was a novel discovery and thus a surprising outcome to clinicians and laypersons alike. Since then, we and other researchers have discovered additional novel biomarkers from fundus photos, such as markers for chronic kidney disease and diabetes, and hemoglobin levels to detect anemia. A unifying goal of work like this is to develop new disease detection or monitoring approaches that are less invasive, more accurate, cheaper and more readily available. How…  ( 7 min )

GeForce NOW gives you the power to game almost anywhere, at GeForce quality. And with the latest controller from SteelSeries, members can stay in control of the action on Android and Chromebook devices. This GFN Thursday takes a look at the SteelSeries Stratus+, now part of the GeForce NOW Recommended program. And it wouldn’t be Read article > The post Take Control This GFN Thursday With New Stratus+ Controller From SteelSeries appeared first on NVIDIA Blog.  ( 3 min )

The hum of a bustling data center is music to an AI developer’s ears — and NVIDIA data centers have found a rhythm of their own, grooving to the swing classic “Sing, Sing, Sing” in this week’s GTC keynote address. The lighthearted video, created with the NVIDIA Omniverse platform, features Louis Prima’s iconic music track, Read article > The post Orchestrated to Perfection: NVIDIA Data Center Grooves to Tune of Millionfold Speedups appeared first on NVIDIA Blog.  ( 4 min )

Introduction Splunk is a well-known log management tool. Splunk mines log from different machines in real-time and can be used to monitor, search, and analyze gathered data. It is a Big Data log management tool that can give insight from the unstructured data stored in the Splunk indexes. Splunk analytics helps turn unstructured log data… Read More »Business Analytics from Application Logs and Database using Splunk The post Business Analytics from Application Logs and Database using Splunk appeared first on Data Science Central.  ( 11 min )

Here’s a curious series for π that I ran across on Math Overflow. In case you’re unfamiliar with the notation, n!! is n double factorial, the product of the positive integers up to n with the same parity as n. More on that here. When n is 0 or -1, n!! is defined to be […] Series for π first appeared on John D. Cook.  ( 1 min )

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Hello, long time lurker here. I've applied to a hackathlon where the goal is to implement an AI for a 1v1 turn based game and was hoping to hear some thoughts, suggestions, recommendations. The game itself will be simple, likely a turn based board game. The AIs battle each other to win the competition. Last year's competition had a simplified version of Catan. I have taken some university courses in the AI field (AI 101, Machine learning, Soft Computing etc.) and am familiar with basic concepts, but I don't know much about RL (I have a general notion on what it's about). I was hoping to learn more about it some day and this competition seems like a perfect opportunity. I'm looking to prepare a bit before the competition and started exploring reinforcement learning methods and frameworks. Is there a framework which would be suitable for this? A framework which has an interface for a custom game that I can build? Could Q learning be used in environments where there is more than one agent? Are there some methods to learn the best heuristic for the minmax algorithm? submitted by /u/ludibog [link] [comments]  ( 1 min )

I am looking for techniques and libraries to interpret/explain the reinforcement learning model. submitted by /u/Mariam_Dundua [link] [comments]

I've implemented a slightly novel distributed DRL system, which I'd like to submit to a conference as a short or demo paper. Does anybody know a conference where this is possible? ICML and Neurips do not support short papers. ICLR has a blog post track. Concerning a full technical paper, I lack the resources (time, computational resources) to thoroughly benchmark my system against seed, ray, apex, r2d2 and so on... submitted by /u/LilHairdy [link] [comments]  ( 1 min )

I was reading this post about the TRPO algorithm. But I couldn't understand how we use MM algorithm in TRPO. I also watched some videos, they talked something about maximizing lower bound but I am not able to catch up what they are explaining. Can anyone explain this to me? submitted by /u/Better-Ad8608 [link] [comments]  ( 1 min )

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Hi, I am trying vanilla policy gradient algorithm of gym's bipedal walker. My algorithm was not converging so I added lstm to the network. I expected that somehow the agent will use the memory to converge even faster but after adding it I observed that LSTM is somehow making the agent worse. Below are the graphs of without and with lstm. ​ without lstm ​ with lstm ​ orange-with lstm and yellow-without lstm Can anyone explain to me why is this happening? submitted by /u/Better-Ad8608 [link] [comments]  ( 1 min )

Extracting knowledge and actionable insights by manually processing hundreds of terabytes of data downlinked from satellites to data centers has become difficult. Synthetic aperture radar (SAR) imaging is a type of active remote sensing in which a satellite sends microwave radar wave pulses down to the Earth’s surface. These radar signals return to the satellite after reflecting off the Earth and any objects. A SAR image is created by processing these pulses over time and space, with each pixel representing the superposition of multiple radar scatters. Radar waves penetrate clouds and illuminate the Earth’s surface even during nights because the satellite is actively creating them. They produce visual that is sometimes contradictory and incompatible with modern computer vision systems. Three common effects are polarisation, layover, and multi-path. The layover effect occurs when radar beams reach the top of a structure before reaching the bottom. This causes the top of the object to appear to be overlapping with the bottom. When radar waves reflect off objects on the ground and bounce numerous times before returning to the SAR sensor, this is known as multi-path effects. Multi-path effects cause things in the picture to appear in multiple transformations in the final image. Continue reading this research summary here BAIR Blog: https://bair.berkeley.edu/blog/2022/03/21/ukraine-sar-maers/ https://i.redd.it/1r05r2mks7p81.gif submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

I see a few articles about Facebook's Pluribus claiming to outperform multiple human pros two years back, but that looks a little dubious to me: The paper explaining it is super basic, which is not what you'd expect from the people who finally solved poker. It has entire pages dedicated to illustrations about the game theory of tic-tac-toe I've seen people arguing about whether it's merely counting cards (which isn't the same as out-strategizing humans) Their metrics for success seem questionable, like they have a graph of cumulative chips won that goes up - which seems great but would also happen if some of the human pros could still outperform it on average What is the actual best we've done at teaching computers poker strategy? submitted by /u/WouldThatIKnew0 [link] [comments]  ( 1 min )

I'm looking for a story about a company that makes a machine trained to write letters. The company (against their rules) decides to connect the machine to the internet and it rapidly becomes smarter than all of humanity and wipes them out in a handful of days. If anyone knows where this story is at, please let me know. Trying to show it to someone, ty! submitted by /u/V4locity [link] [comments]  ( 1 min )

A regular SMS message sent from a business – like a promotion or a welcome message – is a common one-way communication. A chatbot, instead, provides a two-way conversation: it can ask questions, process the answer, and continue the communicational path to subscribe, qualify leads and even sell. But here are things to keep in mind: You'll pay for every message. You should also keep in mind the character limit. A single SMS message should include no more than 160 characters encoded using the GSM-7 character set. If using SMS, you’ll also need to update relevant legal pages like terms of service and privacy policy, with details on how SMS consent will be handled. Many countries may have additional regulations. For the US, all SMS messaging in The United States of America is governed by the CTIA – The Wireless Association. Another point is media limits. SMS does not allow sending videos or attachments. You may send an image with an MMS but keep in mind the cost of it is different than for a text-only SMS. Overall, there are more benefits to SMS chatbots, and if you've been thinking on them for a long time, here's a guide to help you starting out https://botscrew.com/blog/sms-chatbot-a-complete-guide-for-business-use-cases/?utm_source=Reddit&utm_medium=artificial&utm_campaign=44643&utm_term=&utm_content= submitted by /u/Avandegraund [link] [comments]  ( 1 min )

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It is easy, when dealing with data, to think of it as something tangible. More precisely, data as water (or as oil) has become such a powerful metaphor that many managers, and even more than a few more technically-oriented people, have come to accept the fact that data engineering is largely about moving data from… Read More »DSC Weekly Newsletter 22 March 2022: The Shape of Data The post DSC Weekly Newsletter 22 March 2022: The Shape of Data appeared first on Data Science Central.  ( 5 min )

The US Securities and Exchange Commission (SEC), which regulates public company securities, recently proposed its own climate impact reporting requirement. Many US public companies already voluntarily publish information on this topic for shareholders who have been asking for those details. But various state GOP attorneys general are already questioning the SEC’s ability to impose the… Read More »Avoid RegTech myopia with a data-centric approach The post Avoid RegTech myopia with a data-centric approach appeared first on Data Science Central.  ( 6 min )

The previous post covered the problem of oversiloing. Systems thinking, I pointed out, can help reduce the practice of siloing when it’s not necessary.  In earlier posts, I’ve contrasted the difference between provincial IT and data-centric IT:  Provincial IT is no longer necessary given the advances in compute, networking and storage we’ve seen over the… Read More »The long game: Feedback loops and desiloed systems by design (Part II of II) The post The long game: Feedback loops and desiloed systems by design (Part II of II) appeared first on Data Science Central.  ( 5 min )

This is the second post in a two-part series in which I propose a practical guide for organizations so you can assess the quality of text summarization models for your domain. For an introduction to text summarization, an overview of this tutorial, and the steps to create a baseline for our project (also referred to […]  ( 11 min )

When OpenAI released the third generation of their machine learning (ML) model that specializes in text generation in July 2020, I knew something was different. This model struck a nerve like no one that came before it. Suddenly I heard friends and colleagues, who might be interested in technology but usually don’t care much about […]  ( 10 min )

This is a guest post co-authored by Taylor Names, Staff Machine Learning Engineer, Dev Gupta, Machine Learning Manager, and Argie Angeleas, Senior Product Manager at Ibotta. Ibotta is an American technology company that enables users with its desktop and mobile apps to earn cash back on in-store, mobile app, and online purchases with receipt submission, […]  ( 7 min )

Amazon Lex is a service for building conversational interfaces into any application using voice and text. With Amazon Lex, you can easily build sophisticated, natural language, conversational bots (chatbots), virtual agents, and interactive voice response (IVR) systems. You can now use industry grammars to accelerate IVR development on Amazon Lex as part of your IVR […]  ( 9 min )

Posted by Mohammad Saleh, Software Engineer, Google Research, Brain Team and Anjuli Kannan, Software Engineer, Google Docs For many of us, it can be challenging to keep up with the volume of documents that arrive in our inboxes every day: reports, reviews, briefs, policies and the list goes on. When a new document is received, readers often wish it included a brief summary of the main points in order to effectively prioritize it. However, composing a document summary can be cognitively challenging and time-consuming, especially when a document writer is starting from scratch. To help with this, we recently announced that Google Docs now automatically generates suggestions to aid document writers in creating content summaries, when they are available. Today we describe how this was enab…  ( 8 min )

https://youtu.be/X2k7n4FuI7c Cross-modal pre-training has been all the rage lately in deep learning, especially training vision and language models together. However, there are a number of issues, such as low quality datasets that limit the performance of any model trained on it, and also the fact that pure contrastive pre-training cannot be easily fine-tuned for most downstream tasks. BLIP unifies different tasks and objectives in a single pre-training run and achieves a much more versatile model, which the paper immediately uses to create, filter, clean and thus bootstrap its own dataset to improve performance even more! ​ OUTLINE: 0:00 - Intro 0:50 - Sponsor: Zeta Alpha 3:40 - Paper Overview 6:40 - Vision-Language Pre-Training 11:15 - Contributions of the paper 14:30 - Model architecture: many parts for many tasks 19:50 - How data flows in the model 26:50 - Parameter sharing between the modules 29:45 - Captioning & Filtering bootstrapping 41:10 - Fine-tuning the model for downstream tasks ​ Paper: https://arxiv.org/abs/2201.12086 Code: https://github.com/salesforce/BLIP Demo: https://huggingface.co/spaces/Salesforce/BLIP submitted by /u/ykilcher [link] [comments]  ( 1 min )

I am looking for a solution to host an AI art generative ML model for use with an external API. What is the best practice or simplest solution? Here's what I've considered so far: Hugging Face (Gradio) - the VQGAN_CLIP model is down, which doesn't instill confidence... Docker, cog, and replicate.com - it doesn't seem to have API capability. RunwayML - they seem to be pivoting away from such a service. NodeJS and some cloud hosting service (Amazon?) - may be complex to set up, are there any straightforward tutorials to host say VQGAN+CLIP? Would the process be different for a diffusion model? How do tools like WOMBO host their models? Anyone have advice before I start going down a dead end? I have some software engineering and machine learning experience, but none with deployment. Thanks! submitted by /u/WildRaccoon8427 [link] [comments]  ( 1 min )

Hey All! So we've been working on an open-source product to help data scientists build faster. We've revamped our long term road map and we'd like to hear thoughts on our direction and vision. This is the roadmap. Any insights/feedback is much appreciated! submitted by /u/idomic [link] [comments]

With a theoretically large enough dataset of encrypted content corresponding to their unencrypted versions, it it possible to train an AI to develop a method of decryption that’s better than brute-force? If not all, can this work with some encryption methods? submitted by /u/Plane_Bite3639 [link] [comments]  ( 4 min )

f CNNS do not take spatial information into account since filters are looking for features in different locations and output the same feature maps, no matter where the feature is located in the input image, what makes the discriminator robust to real features appearing in random locations ? submitted by /u/Wonderful-Donut-6687 [link] [comments]  ( 3 min )

Any recommendations for tools to check if a paper has been plagiarized? I have an unfortunate situation that may require a thorough examination of another researcher’s work. For example, how did they determine that Siraj Raval’s work was plagiarized? I assume the big ML conferences perform some sort of automated check for plagiarism, right? submitted by /u/purplebrown_updown [link] [comments]  ( 3 min )

Turn on your TV. Fire up your favorite streaming service. Grab a Coke. A demo of the most important visual technology of our time is as close as your living room couch. Propelled by an explosion in computing power over the past decade and a half, path tracing has swept through visual media. It brings Read article > The post What Is Path Tracing? appeared first on NVIDIA Blog.  ( 8 min )

Autonomous vehicle development and validation require the ability to replicate real-world scenarios in simulation. At GTC, NVIDIA founder and CEO Jensen Huang showcased new AI-based tools for NVIDIA DRIVE Sim that accurately reconstruct and modify actual driving scenarios. These tools are enabled by breakthroughs from NVIDIA Research that leverage technologies such as NVIDIA Omniverse platform Read article > The post NVIDIA Showcases Novel AI Tools in DRIVE Sim to Advance Autonomous Vehicle Development appeared first on NVIDIA Blog.  ( 4 min )

This week at GTC, we’re celebrating – celebrating the amazing and impactful work that developers and startups are doing around the world. Nowhere is that more apparent than among the members of our global NVIDIA Inception program, designed to nurture cutting-edge startups who are revolutionizing industries. The program is free for startups of all sizes Read article > The post NVIDIA Inception Introduces New and Updated Benefits for Startup Members to Accelerate Computing appeared first on NVIDIA Blog.  ( 3 min )

Episode 133 | March 23, 2022 In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations. Dr. Gray and her team are working to bring computer science, engineering, social science, and community […] The post Just Tech: Centering Community-Driven Innovation at the Margins episode 1 with Desmond Patton and Mary Gray appeared first on Microsoft Research.  ( 33 min )

Numbers in Morse code seem a little strange. Here they are: |-------+-------| | Digit | Code | |-------+-------| | 1 | .---- | | 2 | ..--- | | 3 | ...-- | | 4 | ....- | | 5 | ..... | | 6 | -.... | | 7 | --... | | 8 […] Morse code numbers and abbreviations first appeared on John D. Cook.  ( 3 min )

Let me start this by saying I am a complete rookie on neural networks. I had one semester when we touched on them and I did a project that involved one. Beyond that, my skill is very limited. Now I am out of school and want to improve on my project. Looking for some expert advice. My project was based on the program found here: Price-Forecaster/Stock-RNN-Deep-Learning-TechIndicators.ipynb at master · marcosan93/Price-Forecaster · GitHub I posted an issue and someone added the comment that the programmer has not split the data to X-train,X-test, y-train,y-test so the model will be always biased due to peek ahead. I added early stopping to the program to help with overfitting. Am I understanding things right on where would you add splitting the data? Would you split the data before running the network and use the training data in the epochs, run the network, then recall the best weights for your test set? Just unsure how you do this when the accuracy of the test set may be garbage. My goal is to send a list of stocks through the network, then give me a "report" at the end that shows which stock is most likely to meet or exceed its prediction. Would this just be a confidence interval? Ideally, I would include a goal return and it would say which stock was most likely to reach that return. Since each stock has its own cycles, I was planning to run each stock through the network to find the individual weights. Any advice on this section? I hope you can help with some advice. I'd love to see how well my added code ideas would do. Thanks. submitted by /u/charlieexcel [link] [comments]  ( 2 min )

The use of AI to write creative stories is increasing in popularity.  ( 3 min )

How does Spotify battle against a giant like Apple? One word: data. With machine learning and AI, Spotify creates value for its users by providing a more personalized and bespoke experience. Let’s take a quick look at the layers of aggregate information that are used to enhance their platform: Spotify uses natural language processing (NLP)… Read More »How Metadata Improves Security, Quality, and Transparency The post How Metadata Improves Security, Quality, and Transparency appeared first on Data Science Central.  ( 5 min )

Introduction/ Problem  Splunk is a well-known log management tool. Splunk mines log from different machines in real-time and can be used to monitor, search, and analyze gathered data. It is a Big Data log management tool that can give insight from the unstructured data stored in the Splunk indexes. Splunk analytics helps turn unstructured log… Read More »Business Analytics from Application Logs and SQL Server Database using Splunk  The post Business Analytics from Application Logs and SQL Server Database using Splunk  appeared first on Data Science Central.  ( 11 min )

For years, organizations have been working steadily to consolidate their data into a single place via a data warehouse, or more recently, a data lake. Data lakes offer key advantages over data warehouses, data marts, and traditional databases that require data to be structured and organized in particular ways. However, businesses have found that they… Read More »Tips for Weaving and Implementing a Successful Data Mesh The post Tips for Weaving and Implementing a Successful Data Mesh appeared first on Data Science Central.  ( 5 min )

In many cases, for an enterprise to build its digital business technology platform, it must modernize its traditional data and analytics architecture. A modern data and analytics platform should be built on services-based principles and architecture. Introduction part 1, provided a conceptual-level reference architecture of a traditional Data and Analytics (D&A) platform. This part, provides… Read More »How to Modernize Enterprise Data and Analytics Platform (Part 2 of 4) The post How to Modernize Enterprise Data and Analytics Platform (Part 2 of 4) appeared first on Data Science Central.  ( 16 min )

AI has been making its way into the marketing world over the last few years. Businesses have touted it as the solution to their problems and a way to incorporate technology into their processes. But, how is AI changing SEO? How can you use AI to improve your business? Machine learning and artificial intelligence are… Read More »AI SEO: How AI Helps You Optimize Content for Search Results The post AI SEO: How AI Helps You Optimize Content for Search Results appeared first on Data Science Central.  ( 4 min )

Every time we think we have grasped a new technology and its use. Sometimes that shift is an increase in the technology itself that seemingly intensifies the original version. Sometimes something happens that causes a significant transformation in the technology’s nature. As the technology’s significance is increasingly understood, the name is altered to better reflect… Read More »Understanding the Role of Augmented Data Catalogs in Data Governance The post Understanding the Role of Augmented Data Catalogs in Data Governance appeared first on Data Science Central.  ( 4 min )

Issues that will affect the metaverse range from hacking to cyberbullying. Proposed solutions are far from perfect. We may see end-to-end encryption disappear soon. The only real solution might be not to enter the metaverse at all. The advent of the metaverse compounds a list of existing security concerns. The addition of another dimension to… Read More »Security Issues in The Metaverse The post Security Issues in The Metaverse appeared first on Data Science Central.  ( 4 min )

This post was co-written by John Heater, SVP of the Contact Center Practice at NeuraFlash. NeuraFlash is an Advanced AWS Partner with over 40 collective years of experience in the voice and automation space. With a dedicated team of conversation designers, data engineers, and AWS developers, NeuraFlash helps customers take advantage of the power of Amazon […]  ( 9 min )

Amazon Search’s vision is to enable customers to search effortlessly. Our spelling correction helps you find what you want even if you don’t know the exact spelling of the intended words. In the past, we used classical machine learning (ML) algorithms with manual feature engineering for spelling correction. To make the next generational leap in […]  ( 7 min )

Is anyone aware of metrics or measures that classify the error relative the complexity of the model. If so could anyone provide some references where people have introduced such a notion? submitted by /u/AbjectDrink3276 [link] [comments]

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GitHub: https://github.com/muxamilian/wysiwyh WYSIWHY is a neural network that transforms an input video into an audio sequence in real time. It does so by compressing each image using an autoencoder and interpreting the resulting code as a frequency range. This could potentially be useful for the visually impaired, helping with indoor navigation. It could also be used to transform an infrared or ultraviolet video to sound and thus enable one to perceive otherwise invisible colors. Demo; recommended with sound turned on In a usual autoencoder, the elements of the code vector are independent of each other. This makes subtle differences between adjacent elements of the code vector hard to perceive for humans. The resulting audio sequence would sound like indistinguishable white noise. For WYSIWYH thus an encoder is used which hierarchically structures the code, meaning that large differences in the input image result in large differences in the output code. This also results in adjacent elements of the code vector being correlated. This makes the autoencoder’s code more friendly to human perception. Overall approach submitted by /u/muxamilian [link] [comments]  ( 2 min )

https://arxiv.org/abs/2203.10977 submitted by /u/gaggi_94 [link] [comments]

We're happy to announce the release of STUMPY v1.11.0! This version includes the oft requested Minkowski (p-norm) Distance, support for Multi-dimensional Motif Discovery, new Annotation vector tutorials, and enhancements for Pan Matrix Profiles! https://github.com/TDAmeritrade/stumpy submitted by /u/slaw07 [link] [comments]  ( 1 min )

FDG2022: Foundations of Digital Games 2022Athens, Greece, September 5-8, 2022Conference website: http://fdg2022.org/ Foundations of Digital Games (FDG) 2022 invites research contributions in the form of papers, posters and demos, doctoral consortium applications, as well as panel, competition, and workshop proposals. We invite contributions from within and across any discipline committed to advancing knowledge on the foundations of games: computer science and engineering, humanities and social sciences, arts and design, mathematics and natural sciences. As was the case in the previous years, we aim to publish the FDG 2022 proceedings in the ACM Digital Library. ​FDG invites authors to submit short or full papers reporting new research. Both short and full papers need to be anonymized and…  ( 1 min )

Flower and its community are growing. Since Flower is a friendly federated learning framework, the goal is always to get an easy start to federated learning for every data scientist. This involves having Flower coding examples for different machine learning frameworks. One of the frameworks is JAX which was developed by Google researchers to run NumPy programs on GPUs and TPUs. It is quickly rising in popularity and is used by DeepMind to support and accelerate its research. We couldn’t miss the opportunity to create a code example and a blog post about “JAX meets Flower - Federated Learning with JAX”. It takes always some time to get into a new machine learning framework and its syntax but it is easy to combine it with Flower. You can check out the blog post here: https://flower.dev/blog/2022-03-22-running-jax-federated-jax-meets-flower/ If you are interested in writing a blog post for our Flower community, contact me. submitted by /u/burnai [link] [comments]  ( 1 min )

Hi! I have some experience deploying batch machine learning models and now I want to learn about real-time models. More specifically, how to put them in production and what are the best practices and tools for different use-cases. Any ideas? I was thinking of reading the book "Designing Event-Driven Systems" by Ben Stopford (I think it's based on Kafka which seems quite popular), but would like to hear your thoughts or if someone has any other reference. Thanks and I hope this is the right sub! submitted by /u/Silver_Book_938 [link] [comments]  ( 1 min )

We developed a social platform for everyone to rate and review machine learning research papers. One can think of it as the IMDB database for the research community. Link: https://doublind.com Features we think that are important: Search all machine learning related papers, old and new. Rate and review the paper you've just read, open or anonymous. A profile page is created for all users to display their paper reviews. Each paper has a rating score that help users find good papers to read. Users can like, comment and share a paper review, helping it reaching more readers. ​ We'd love to hear your feedback and suggestions. Thank you all and we appreciate the support. submitted by /u/DouBlindDotCOM [link] [comments]  ( 4 min )

How do you guys deal with the stress of developing a new models and being off from SOTA but also needing to publish papers? Do people publish results that aren’t quite SOTA? Do you tweak results? Do you do something else? submitted by /u/AbjectDrink3276 [link] [comments]  ( 3 min )

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I’m a tech in an academic computational lab at a very large US flagship R1 university. My current lab is a neuroscience lab that does lots of ML theory stuff (bio inspired ML). My primary goal is to get a PhD and find a research scientist position in industry at a non-FAANG company. Because my PI is a neuroscientist, I might not be able to work with him as a PhD student if I apply to the EE or CS program at this university, but he has an affiliate appointment at the Biomedical Engineering department. Would having a biomedical engineering degree in any way affect my ability to get a research scientist ML job? As long as I have a productive PhD with ML publications etc., and get internships at relevant places, I should be fine right? Will I have a worse chance at non-biotech companies? If BME is an issue, I’m sure my PI can get affiliate status in EE, because he has multiple collaborators in EE. submitted by /u/throawaythroaway11 [link] [comments]  ( 5 min )

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Hi all, I'm trying to code an actor net that consists of an LSTM followed by two fully connected layers. The policy is therefore parameterized using an LSTM. The LSTM is meant to enable agents to remember the history of states in which they have observed other agents, and improve their ability to model other agents’ policies. Can someone refer me to PyTorch code for such an architecture? Thanks! submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

Hey there! 👋 I'm Thomas Simonini from Hugging Face 🤗. I work on building tools, environments and integrating RL libraries to empower researchers and RL enthusiasts. I was wondering how Hugging Face can be useful to you in the Deep Reinforcement Learning Ecosystem? What do you need as RL researcher/enthusiast/engineer and how we can help you? For now: We integrated Stable-baselines3 to the Hub** such that you can: Easily host and test your saved models. Load powerful, trained models from the community https://preview.redd.it/n0b2s1gndyo81.jpg?width=1920&format=pjpg&auto=webp&s=eb62a1f4323b12a5c1eb9d7bcb44ebbc6bae579f We're currently integrating more libraries (RL-Zoo, CleanRL...) We're working on building tools that allow you to generate a replay video of your agent and test it. We're building open-source RL environments such as snowball-fight And finally, we're working on state of the art's research with Decision Transformers, Embodied environments, etc. But I would love to know what do you need as RL researcher/enthusiast/engineer and how we can help you? Thanks for your feedback, 📢 To keep in touch is to join our discord server to exchange with us and with the community. submitted by /u/cranthir_ [link] [comments]  ( 3 min )

I use the [RL Robotics library](https://github.com/Farama-Foundation/Gym-Robotics) to simulate robots. In the Fetchxxx-Env, the variable distance_threshold is set as 0.05 default to determine whether it completed successfully a task. I try to change it by using env.distance_threshold = 0.01(or other value) Because I think 0.05(m) is too rough for some tasks. But it doesn't work! (When the success_rate is 1.0, the distance between the achieved_goal and the desired_goal is 0.04x, which is larger than I set.) What should I do to change the conditions for determining whether a task is complete? Can anyone give me some advice or information? submitted by /u/Due_Advertising6542 [link] [comments]  ( 1 min )

Hello, I'm an undergraduate CS student trying to re-implement this paper for my University project. I have some experience in Machine Learning (Supervised Learning) and implemented some basic projects myself, but I'm a little bit confused about where to begin, I've found two different implementations online but I don't think that's gonna help me a lot as I'm not that experienced in this field. I would appreciate any suggestions on how I can understand the paper and implement it myself (not just a copy/paste from someone else's code) or the libraries that can help me, etc. Sorry for my bad English, I'm not a native speaker thanks a lot :) submitted by /u/ahmadreza_hadi [link] [comments]  ( 1 min )

I'm curious about your opinion on this. There are impressive recent advances in DRL for humanoid whole-body control (Deepmimic, Drecon, etc...). Do you think these methods will one day overtake the "classical" approaches from control theory? Or we will see more and more methods combining these two approaches? submitted by /u/thejackforest [link] [comments]  ( 2 min )

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Generative Adversarial Networks (GANs), with their capacity of producing high-quality images, have been the leading technology in image generation for the past couple of years. Nevertheless, their minimax learning mechanism brought out different limits, such as training instability and mode collapse (i.e., when all the produced samples belong to a small set of samples). Recently, Generative Transformer models are beginning to match, or even surpass, the performances of GANs. The simple idea is to learn a function to encode the input image into a quantized sequence and then train an autoregressive Transformer on a sequence prediction task (i.e., predict an image token, given all the previous image tokens). This learning is based on maximum likelihood and thus not affected by the same issue…  ( 2 min )

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How does nebullvm work? It takes your AI model as input and outputs an optimized version that runs 5-20 times faster on your hardware. In other words, nebullvm tests multiple deep learning compilers to identify the best possible way to execute your model on your specific machine, without impacting the accuracy of your model. And that's it. In just a few lines of code. And a big thank you to everyone for supporting this open-source project! The library received 250+ Github stars⭐ on release day, and that's just amazing 🚀 ORIENTATION MAP Let's learn more about nebullvm and AI optimization. Where should we start? From... Some CONTEXT on why few developers optimize AI and related negative consequences An overview of how the LIBRARY works Some USE CASES, technology demonstrations and…  ( 6 min )

Im wondering if there is any ai that you can feed your voice to and it will try to make an approximation of what you sound like to yourself in your head? submitted by /u/alidan [link] [comments]  ( 1 min )

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Hi guys!, we are University Students and we're currently looking for a speaker that can discuss Artificial Intelligence on an introductory level and the Opportunities it can bring for Engineering Students. Thank you! Please DM asap me for more details. submitted by /u/BigBadToilet [link] [comments]

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At GTC, NVIDIA announced significant updates for millions of creators using the NVIDIA Omniverse real-time 3D design collaboration platform. The announcements kicked off with updates to the Omniverse apps Create, Machinima and Showroom, with an immement View release. Powered by GeForce RTX and NVIDIA RTX GPUs, they dramatically accelerate 3D creative workflows. New Omniverse Connections Read article > The post NVIDIA Omniverse Upgrade Delivers Extraordinary Benefits to 3D Content Creators appeared first on NVIDIA Blog.  ( 5 min )

Digital artists and creative professionals have plenty to be excited about at NVIDIA GTC. Impressive NVIDIA Studio laptop offerings from ASUS and MSI launch with upgraded RTX GPUs, providing more options for professional content creators to elevate and expand creative possibilities. NVIDIA Omniverse gets a significant upgrade — including updates to the Omniverse Create, Machinima Read article > The post At GTC: NVIDIA RTX Professional Laptop GPUs Debut, New NVIDIA Studio Laptops, a Massive Omniverse Upgrade and NVIDIA Canvas Update appeared first on NVIDIA Blog.  ( 6 min )

Promising to transform trillion-dollar industries and address the “grand challenges” of our time, NVIDIA founder and CEO Jensen Huang Tuesday shared a vision of an era where intelligence is created on an industrial scale and woven into real and virtual worlds. Kicking off NVIDIA’s GTC conference, Huang introduced new silicon — including the new Hopper Read article > The post Keynote Wrap Up: Turning Data Centers into ‘AI Factories,’ NVIDIA CEO Intros Hopper Architecture, H100 GPU, New Supercomputers, Software appeared first on NVIDIA Blog.  ( 8 min )

The University of Florida’s academic health center, UF Health, has teamed up with NVIDIA to develop a neural network that generates synthetic clinical data — a powerful resource that researchers can use to train other AI models in healthcare. Trained on a decade of data representing more than 2 million patients, SynGatorTron is a language Read article > The post Unlimited Data, Unlimited Possibilities: UF Health and NVIDIA Build World’s Largest Clinical Language Generator appeared first on NVIDIA Blog.  ( 4 min )

Remote work and hybrid workplaces are the new normal for professionals in many industries. Teams spread throughout the world are expected to create and collaborate while maintaining top productivity and performance. Businesses use the NVIDIA RTX platform to enable their workers to keep up with the most demanding workloads, from anywhere. And today, NVIDIA is Read article > The post New NVIDIA RTX GPUs Tackle Demanding Professional Workflows and Hybrid Work, Enabling Creation From Anywhere appeared first on NVIDIA Blog.  ( 4 min )

Four NVIDIA Inception members have been selected as the first cohort of startups to access Cambridge-1, the U.K.’s most powerful supercomputer. The system will help British companies Alchemab Therapeutics, InstaDeep, Peptone and Relation Therapeutics enable breakthroughs in digital biology. Officially launched in July, Cambridge-1 — an NVIDIA DGX SuperPOD cluster powered by NVIDIA DGX A100 Read article > The post First Wave of Startups Harnesses UK’s Most Powerful Supercomputer to Power Digital Biology Breakthroughs appeared first on NVIDIA Blog.  ( 4 min )

When it comes to creating and connecting virtual worlds, over 150,000 individuals have downloaded NVIDIA Omniverse to make huge leaps in transforming 3D design workflows and achieve new heights of real-time, physically accurate simulations. At GTC, NVIDIA today announced new releases and updates for Omniverse — including the latest Omniverse Connectors and libraries — expanding Read article > The post NVIDIA Omniverse Ecosystem Expands 10x, Amid New Features and Services for Developers, Enterprises and Creators appeared first on NVIDIA Blog.  ( 4 min )

Next time socks, cereal or sandpaper shows up in hours delivered to your doorstep, consider the behind-the-scenes logistics acrobatics that help get them there so fast. Order fulfillment is a massive industry of moving parts. Heavily supported by autonomous mobile robots (AMRs), warehouses can span 1 million square feet, expanding and reconfiguring to meet demands. Read article > The post NVIDIA Unveils Isaac Nova Orin to Accelerate Development of Autonomous Mobile Robots appeared first on NVIDIA Blog.  ( 3 min )

Lucid Group may be a newcomer to the electric vehicle market, but its entrance has been grand. The electric automaker announced at GTC that its current and future fleets are built on NVIDIA DRIVE Hyperion for programmable, intelligent capabilities. By developing on the scalable, software-defined platform, Lucid ensures its vehicles are always at the cutting Read article > The post Driving on Air: Lucid Group Builds Intelligent EVs on NVIDIA DRIVE appeared first on NVIDIA Blog.  ( 2 min )

NVIDIA DRIVE Hyperion and DRIVE Orin are gaining ground in the industry. At NVIDIA GTC, BYD, the world’s second-largest electric vehicle maker, announced it is building its next-generation fleets on the DRIVE Hyperion architecture. This platform, based on DRIVE Orin, is now in production, and powering a wide ecosystem of 25 EV makers building software-defined Read article > The post NVIDIA DRIVE Continues Industry Momentum With $11 Billion Pipeline as DRIVE Orin Enters Production appeared first on NVIDIA Blog.  ( 3 min )

With a detailed knowledge of the world and everything in it, maps provide the foresight AI uses to make advanced and safe driving decisions. At his GTC keynote, NVIDIA founder and CEO Jensen Huang introduced NVIDIA DRIVE Map, a multimodal mapping platform designed to enable the highest levels of autonomy while improving safety. It combines Read article > The post Announcing NVIDIA DRIVE Map: Scalable, Multi-Modal Mapping Engine Accelerates Deployment of Level 3 and Level 4 Autonomy appeared first on NVIDIA Blog.  ( 4 min )

NVIDIA DRIVE Hyperion is taking software-defined vehicle architectures to the next level. At his GTC keynote, NVIDIA founder and CEO Jensen Huang announced DRIVE Hyperion 9, the next generation of the open platform for automated and autonomous vehicles. The programmable architecture, slated for 2026 production vehicles, is built on multiple DRIVE Atlan computers to achieve Read article > The post Introducing NVIDIA DRIVE Hyperion 9: Next-Generation Platform for Software-Defined Autonomous Vehicle Fleets appeared first on NVIDIA Blog.  ( 3 min )

Siemens Gamesa Renewable Energy is working with NVIDIA to create physics-informed digital twins of wind farms — groups of wind turbines used to produce electricity. The company has thousands of turbines around the globe that light up schools, homes, hospitals and factories with clean energy. In total they generate over 100 gigawatts of wind power, Read article > The post Siemens Gamesa Taps NVIDIA Digital Twin Platform for Scientific Computing to Accelerate Clean Energy Transition appeared first on NVIDIA Blog.  ( 3 min )

We’re working with leaders in quantum computing to build the tools developers will need to program tomorrow’s ultrahigh performance systems. Today’s high-performance computers are simulating quantum computing jobs at scale and with performance far beyond what’s possible on today’s smaller and error-prone quantum systems. In this way, classical HPC systems are helping quantum researchers chart Read article > The post NVIDIA Unveils Onramp to Hybrid Quantum Computing appeared first on NVIDIA Blog.  ( 3 min )

AT&T’s wireless network connects more than 100 million subscribers from the Aleutian Islands to the Florida Keys, spawning a big data sea. Abhay Dabholkar runs a research group that acts like a lighthouse on the lookout for the best tools to navigate it. “It’s fun, we get to play with new tools that can make Read article > The post Speed Dialer: How AT&T Rings Up New Opportunities With Data Science appeared first on NVIDIA Blog.  ( 3 min )

The NVIDIA Hopper GPU architecture unveiled today at GTC will accelerate dynamic programming — a problem-solving technique used in algorithms for genomics, quantum  computing, route optimization and more — by up to 40x with new DPX instructions. An instruction set built into NVIDIA H100 GPUs, DPX will help developers write code to achieve speedups on Read article > The post NVIDIA Hopper GPU Architecture Accelerates Dynamic Programming Up to 40x Using New DPX Instructions appeared first on NVIDIA Blog.  ( 3 min )

The largest AI models can require months to train on today’s computing platforms. That’s too slow for businesses. AI, high performance computing and data analytics are growing in complexity with some models, like large language ones, reaching trillions of parameters. The NVIDIA Hopper architecture is built from the ground up to accelerate these next-generation AI Read article > The post H100 Transformer Engine Supercharges AI Training, Delivering Up to 6x Higher Performance Without Losing Accuracy appeared first on NVIDIA Blog.  ( 4 min )

Everyone wants to be heard. And with more people than ever in video calls or live streaming from their home offices, rich audio free from echo hiccups and background noises like barking dogs is key to better sounding online experiences. NVIDIA Maxine offers GPU-accelerated, AI-enabled software development kits to help developers build scalable, low-latency audio Read article > The post NVIDIA Maxine Reinvents Real-Time Communication With AI appeared first on NVIDIA Blog.  ( 5 min )

Translator, a Microsoft Azure Cognitive Service, is adopting Z-code Mixture of Experts models, a breakthrough AI technology that significantly improves the quality of production translation models. As a component of Microsoft’s larger XYZ-code initiative to combine AI models for text, vision, audio, and language, Z-code supports the creation of AI systems that can speak, see, […] The post Microsoft Translator enhanced with Z-code Mixture of Experts models appeared first on Microsoft Research.  ( 5 min )

Cloud computing is powering a new age of data and AI by democratizing access to scalable compute, storage, and networking infrastructure and services. Thanks to the cloud, organizations can now collect data at an unprecedented scale and use it to train complex models and generate insights.   While this increasing demand for data has unlocked new […] The post Powering the next generation of trustworthy AI in a confidential cloud using NVIDIA GPUs appeared first on Microsoft Research.  ( 9 min )

I am making a mobile app which contains newsfeed page, where I want to show the users post in ranking order, posts can be ranked based on the post content and the user rating count and feedback who has posted it, I don't want to spend more time on building a ranking system so what way or pathway I can take? Is there any API, system or anything already built which I can integrate into my app directly to rank post? submitted by /u/aliazlanaziz [link] [comments]  ( 1 min )

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A few days ago I wrote about Frequency Shift Keying (FSK), a way to encode digital data in an analog signal using two frequencies. The extension to multiple frequencies is called, unsurprisingly, Multiple Frequency Shift Keying (MFSK). What is surprising is how MFSK sounds. When I first heard MFSK I immediately recognized it as an […] Multiple Frequency Shift Keying first appeared on John D. Cook.  ( 3 min )

Henry Lowengard left a comment on my post Phone tones in musical notation mentioning dial tones and busy signals, so I looked these up. Tones According to this page, a dial tone in DTMF [1] is a chord of two sine waves at 350 Hz and 440 Hz. In musical notation: According to the same […] Dial tone and busy signal first appeared on John D. Cook.  ( 1 min )

Using the Artificial Neural Network (ANN) to make a churn model, we will create a model that predicts a handwritten digit. (with source…  ( 3 min )

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I am looking to generate new car wheel designs based on a dataset I have of my own 30-50 wheels. I was wondering if there is a tool/GAN that would suit this? submitted by /u/jrobin51 [link] [comments]

Artificial intelligence is generally praised for their use in many sectors. One of the most notable achievements of AI in science is its breakthrough in molecular biology as it helped reduce the time to identify the structure of complicated 3D protein folds. AI has also helped advance the efficiency in quantum computers, and now AI is making a large presence in clinical/healthcare. Despite the potential of AI being revolutionary in healthcare we must be cautious utilizing it. I want to highlight the advantages and disadvantages of AI to inspire and to set forth challenges for you to approach in the future. The advantage of AI in healthcare is its versatile applications. You can find developments in many topics of healthcare such as drug discovery, image processing, data analysis, and mor…

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​ https://reddit.com/link/tjfnnz/video/e1hw1trsiro81/player submitted by /u/Nitorblog [link] [comments]  ( 1 min )

Hi all, I've been interested in pattern theory for a bit, and as far as I can tell it can be viewed as a framework for artificial intelligence. In Grenander's book "Calculus of Thoughts" David Mumford mentions that a major issue with pattern theory is the creation of generators and modalities of bonds. For the bonds, has anyone tried basing their properties on a sense of time? I.e. in whatever environment a program is in, it would have a clock measuring time, and would somehow equate these bonds simply with events happening close to each other in time. Apologies if this is a naïve/vague/nonsensical question, I'm enthusiastic but not too knowledgeable about pattern theory. Any feedback is greatly appreciated submitted by /u/patterntheoryacc [link] [comments]  ( 1 min )

I need an Ai image generator that takes an image and generates what is beyond the border of that image. for example if i have an image of the front half of a dog, it fills in the other half of the dog im going to use it to extend a picture of a building btw submitted by /u/Bingela_ [link] [comments]  ( 1 min )

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View Poll submitted by /u/futureanalytica [link] [comments]  ( 1 min )

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I know the question sounds funny but I mean it seriously. Is there something trained like this, that could be easily plugged to something like FFmpeg? Doing it on every frame would probably be too slow, but one could probably do it only on P-frames with a very small increase of error rate. submitted by /u/flying-benedictus [link] [comments]  ( 1 min )

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Researchers from LinkedIn open-source the FastTreeSHAP package which is a Python module based on the paper ‘Fast TreeSHAP: Accelerating SHAP Value Computation for Trees.’ Implementing the widely-used TreeSHAP algorithm in the SHAP package allows for the efficient interpretation of tree-based machine learning models by estimating sample-level feature significance values. Its package includes two new algorithms: FastTreeSHAP v1 and FastTreeSHAP v2, both of which improve TreeSHAP’s computational efficiency by taking a different approach. The empirical benchmarking tests show that FastTreeSHAP v1 is 1.5x faster than TreeSHAP while keeping memory costs the same, and FastTreeSHAP v2 is 2.5x faster while using slightly more memory. The FastTreeSHAP package fully supports parallel multi-core computing to speed up its computation. Continue Reading The Full Summary Article Paper: https://arxiv.org/pdf/2109.09847.pdf Github: https://github.com/linkedin/fasttreeshap submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

I asked this question on stats stackexchange, and it is posted here. I don't copy it here because formulas don't show up nicely on reddit. I am trying to implement this paper by Fallah et al (NIPs 2021) titled: On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. As the title suggests, they propose an algorithm for meta RL that uses an stochastic approximation of the gradient. My problem is with the term that yields the probability of a given trajectory (a sequence of state-actions). I don't know how to estimate that term and the paper doesn't discuss that. I'd appreciate if anyone can share any insight on how to estimate that term. submitted by /u/carlml [link] [comments]  ( 1 min )

Hi everyone! I'm working on a project where I'm using neural networks to overcome some RF impairments in a wireless communications system. When I introduce a frequency offset to the data, my current neural network (which is a simple 2 hidden layer feed-forward NN) can not compensate. The frequency offset is basically a phase rotation on the IQ samples. The problem is this is also time-dependent such that the amount the sample is shifted is a function of time. ie: x_out[n] = x[n]e^(j2*pi*w*n) where w is the phase shift from the frequency offset, n is the discrete sample. I know my NN needs some kind of memory (so I should use an LSTM or some RNN variant), just wondering if anyone has some recommended papers or has done some work similar to this and would be willing to help, thanks! submitted by /u/mtot10 [link] [comments]  ( 1 min )

Hi everyone! With a friend, we are develpoing a tool to be able to easily start pods, distribute the code (with magics) and stop them directly from your notebook. Pods can be started in seconds with the specifications you need: number of instances/pods, CPU, Memory, GPU and packages The tool makes it easy to increase computer resources quickly, code locally and consume resources only when you need it, paralelize tasks and do parameter grid search. If you like it, you can visit our landing page and register for early access: telekinesis.cloud We are giving 10 GPU hours to our first 50 registered users! We hope you like it! Thank you! Please share feedback submitted by /u/snuns90 [link] [comments]  ( 1 min )

CALL FOR PAPERSEvoRL 2022Evolutionary Reinforcement Learning workshop at GECCO 2022, July 9-13, Boston, USA In recent years reinforcement learning (RL) has received a lot of attention thanks to its performance and ability to address complex tasks. At the same time, multiple recent papers, notably work from OpenAI, have shown that evolution strategies (ES) can be competitive with standard RL algorithms on some problems while being simpler and more scalable. Similar results were obtained by researchers from Uber, this time using a gradient-free genetic algorithm (GA) to train deep neural networks on complex control tasks. Moreover, recent research in the field of evolutionary algorithms (EA) has led to the development of algorithms like Novelty Search and Quality Diversity, capable of eff…  ( 2 min )

Hi, As the title points out, I would like to identify someone's professional background (e.g. engineering, art, ...) through his syntax. To do so my strategy was to find a dataset regrouping messages alongside the person's background but it doesn't exist (to my knowledge) Do you guys have any idea to persue? (any good dataset?, trying to find multiple datasets and merging them?, scraping LinkedIn?) Is it even possible to e.g. scrape LinkedIn for such information? Any thoughts and feedback are welcome! Initial strategy: - Either finding a dataset or scraping LinkedIn. (is it even possible?) - Training a NN. Alternative question: - Do you know of any other approach that would be less data intensive using for instance hand crafted features, computational linguistics and the such? Cheers, submitted by /u/Inquation [link] [comments]  ( 2 min )

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I have made an animated video (https://www.youtube.com/watch?v=Rl-sFaE1z4M) for our ICLR 2022 paper (https://arxiv.org/abs/2203.09630). Check it out if you are interested. I have made the video using 3b1b's manim library (https://github.com/ManimCommunity/manim). Feedback is always very welcome! submitted by /u/Human-Career-9962 [link] [comments]  ( 1 min )

LinkedIn open sources the FastTreeSHAP Python package for efficient interpretation of tree-based ML models (XGBoost, LightGBM, sklearn random forest) using SHAPLEY. FastTreeSHAP v2 would be 2.5x faster than TreeSHAP. Let's reminder that SHAP (SHapley Additive exPlanation) values quantify the contribution of each feature to the model prediction, a bit like how each player contributes to the success of a sports team. SHAP does it by incorporating concepts from game theory and local explanations. Naively implemented, SHAP takes exponential time. LinkedIn blog post, scientific paper, and GitHub repo with IPython Notebooks. submitted by /u/ClaudeCoulombe [link] [comments]  ( 1 min )

Hey, all. Hope you lot are doing alright. I was looking into Explainable AI and model interpretability lately, and I had an idea but am wondering whether it would constitute a valid use case. There is a data analysis project happening at work where we're trying to analyze data we had on hand to determine the factors that affect our KPOs and possibly derive useful actionable insights. Instead of moving forward with manually evaluating correlation and doing EDA that way, I was thinking of using variable importance measures, subpopulation analyses, and partial dependence profiles to potentially gain insights, or at the very least narrow down exploratory scope. I have been scrounging the internet for someone using white box models for expediting analysis tasks, but I haven't found anything that remotely fits the bill except perhaps this article: https://medium.com/swlh/how-i-used-a-machine-learning-model-to-generate-actionable-insights-3aa1dfe2ddfd It'd be great if you guys let me know what you think. Cheers, submitted by /u/Impartial_Bystander [link] [comments]  ( 1 min )

Is there a direct link or explanation on why the predictive variance of a Bayesian model can be interpreted as epistemic uncertainty? Many epistemic uncertainty quantification methods, for example, MC-Dropout, Deep Ensemble, claim themselves as a Bayesian model to justify their approach for quantifying uncertainty. Let's just say their claims are valid so that the methods are truly Bayesian. But then, why are Bayesian methods supposed to estimate uncertainty well? submitted by /u/swyoon_ [link] [comments]  ( 1 min )

Hi everyone, Imagine there’s a rainfall dataset, and there’s surely extreme values in the dataset. These extreme values are important because I would need them to predict extreme events. Unfortunately, when doing normalisation, these extreme values will tend to result in almost every other values being extremely small after min-max. Any suggestions to go about this? This is for research submitted by /u/plsendfast [link] [comments]  ( 2 min )

An author interview on the Equivariant Subgraph Aggregation Networks paper. Discusses why the expressive power of GNNs is limited and a method for breaking the bottleneck of the 1-WL algorithm https://youtu.be/VYZog7kbXks submitted by /u/zjost85 [link] [comments]

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I am using stabel-baselines frameworks to train the model. To build an agent environment I am using Gym environment. I want to make sure that results do not change very much when I run the same model a second time. How is it possible? submitted by /u/Mariam_Dundua [link] [comments]  ( 1 min )

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CALL FOR PAPERS EvoRL 2022 Evolutionary Reinforcement Learning workshop at GECCO 2022, July 9-13, Boston, USA In recent years reinforcement learning (RL) has received a lot of attention thanks to its performance and ability to address complex tasks. At the same time, multiple recent papers, notably work from OpenAI, have shown that evolution strategies (ES) can be competitive with standard RL algorithms on some problems while being simpler and more scalable. Similar results were obtained by researchers from Uber, this time using a gradient-free genetic algorithm (GA) to train deep neural networks on complex control tasks. Moreover, recent research in the field of evolutionary algorithms (EA) has led to the development of algorithms like Novelty Search and Quality Diversity, capable of…  ( 2 min )

Dynamics in reinforcement learning, that are represented by the transition function in an MDP, are meant to modelize the probability of reaching (or deriving from) the desired state. From what I understand, this probability is caused by the environement or by a malfunction within the agent(?). I would like to ask if there is any real life problem modelized into an RL model with a real life transition probability? I would be super grateful if you redirect me to research papers in this axis (all I found so far are theoretical representation of dynamics) Thanks in advance. submitted by /u/Unfinished-plans [link] [comments]  ( 3 min )

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This post expands on a small part of the post Demystifying the Analemma by M. Tirado. Apparent solar declination given δ by δ = sin-1( sin(ε) sin(θ) ) where ε is axial tilt and θ is the angular position of a planet. See Tirado’s post for details. Here I want to unpack a couple things […] Solar declination first appeared on John D. Cook.  ( 1 min )

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The field of Artificial Intelligence (AI) continues to expand and improve by leaps and bounds. Today’s AI applications are becoming smarter…  ( 3 min )

Figure 1: Airmass measurements over Ukraine from February 18, 2022 - March 01, 2022 from the SEVIRI instrument. Data accessed via the EUMETSAT Viewer. Satellite imagery is a critical source of information during the current invasion of Ukraine. Military strategists, journalists, and researchers use this imagery to make decisions, unveil violations of international agreements, and inform the public of the stark realities of war. With Ukraine experiencing a large amount of cloud cover and attacks often occuring during night-time, many forms of satellite imagery are hindered from seeing the ground. Synthetic aperture radar imagery penetrates cloud cover, but requires special training to interpret. Automating this tedious task would enable real-time insights, but current computer vision meth…  ( 11 min )

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So I am trying to make a neural network which would predict if it's raining or not. However I'm having problems with the neural network because it seems to not learn at all. Or at least it gives very low propability for any data which I try to predict. The dataset contains temperature, pressure, and relative humidity which are mesaured every 10 minutes. The data is from Finnish Weather forecast center (Ilmatieteenlaitos). I'm also modifying the data so that I can feed 24h of values for each rainfall value in to the neural network. Here is a photo explaining this better https://imgur.com/a/jFpsN8Q and here is the code for the project https://www.kaggle.com/code/juhosyvjrvi/rainfall-prediction/notebook Thank you for any help in advance! submitted by /u/Juuhis999 [link] [comments]  ( 1 min )

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I was wondering if anyone has any code they use in python to visualize attention head maps for transformer architectures. If so are you willing to share it? submitted by /u/AbjectDrink3276 [link] [comments]  ( 1 min )

Does RepL4NLP accept papers such as pruning and quantization? The link below gives you a list of topics if you scroll down. One of them was " Efficient learning of representations and inference: with respect to training and inference time, model size, amount of training data, etc.". I was wondering if that has anything to do with pruning and/or quantization? ​ https://sites.google.com/view/repl4nlp2022/ submitted by /u/SiegeMemeLord [link] [comments]  ( 1 min )

Hi everyone. I am looking for reaearch papers and ideas on how to solve this problem for my master’s thesis. Any pointers to SOTA reearch would be greatly appreciated. Let me just quickly summarize the problem at hand: There are over 100k devices connected to this cloud network, where the devices track the GPS location of shipping containers. Most of the containers also have a cooling system, where it’s possible to read a couple of sensor data and also remotely control temperature/humidity in the containers. My main objective is to apply some ML algo to try and predict if some device (or the integrated cooling system) is about become faulty, such that it can trigger an alarm and consequently get someone to inspect the devices due to possible malfunction. The approach: Just on top of my mind, I would like to apply some unsupervised learning technique to detect if some device is behaving far from the norm. I am even more intetested in supervised learning, but it might be a little tricky to tell based on a record if it’s faulty (target variable) Any ideas? Feel free to ask if something isn’t clear. Training a model on 100TB of data might become a challenge in itself. I would have to apply some algo that can learn iteratively. Thanks! submitted by /u/shotez [link] [comments]  ( 2 min )

Good day, my fellow researchers and engineers. Most of the unstructured pruning algorithms cannot induce their theoretical performance due to the fact that we cannot ignore the zeros when the actual computation occurs. I recently read the paper about N:M structured sparse neural networks and I just got interested in the techniques and research about 'the actual acceleration for zeroes'. If we want to dig more about these kinds of sparsity acceleration ( If I'm calling it right), which paper should we look into? How can we ignore the zeroes, aside from saving indices of non-zero values? submitted by /u/KindAd9527 [link] [comments]  ( 1 min )

Hey everyone, it's notoriously hard to find bugs in ML models because often times they are silent. I've created a VSCode extension that can scan your Python functions and find bugs. Under the hood it utilizes a ML model that tries to understand your intent and provide suggestions. Please, could you give me some feedback? I would really appreciate any help. Thanks a lot in advance! Website: https://tensorbox.ai Video demo: https://youtu.be/MSpsCpPZokY submitted by /u/CosmicRaysGalaxy [link] [comments]  ( 1 min )

In this short blog, we’ll review the process of taking a POC data science pipeline (ML/Deep learning/NLP) that was conducted on Google Colab, and transforming it into a pipeline that can run parallel at scale and works with Git so the team can collaborate on. Background Google Colab is pretty straightforward, you can open a… Read More »Google Colab to a Ploomber pipeline: ML at scale The post Google Colab to a Ploomber pipeline: ML at scale appeared first on Data Science Central.  ( 3 min )

The Stanford HAI (Human cantered artificial intelligence) report is out. I track this report every year and it always has some good insights. The report is a bit more focused on large language models and their impact – but also covers key trends The main findings are Private investment in AI soared while investment concentration… Read More »Biases in CLIP and the Stanford HAI report The post Biases in CLIP and the Stanford HAI report appeared first on Data Science Central.  ( 2 min )

Data and Analytics Platform is a sub-platform of the Enterprise Digital Business Technology Platform. It contains information management and analytical capabilities. Introduction Building the digital business technology platform is a core aspect of enterprise endeavors to support its digital business transformation and therefore gain a sustainable competitive advantage. The digital business technology platform includes 5… Read More »How to Modernize Enterprise Data and Analytics Platform (Part 1 of 4) The post How to Modernize Enterprise Data and Analytics Platform (Part 1 of 4) appeared first on Data Science Central.  ( 6 min )

The most annoying thing about Fourier analysis is that there are many slightly different definitions of the Fourier transform. One time I got sufficiently annoyed that I creates a sort of Rosetta Stone of Fourier theorems under eight different conventions. Later I discovered that Mathematica supports these same eight definitions, but with slightly different notation. […] Reverse engineering Fourier conventions first appeared on John D. Cook.  ( 3 min )

Like many others on here, I've hit a crossroad in life and I'm unsure whether I want to do a PhD or stay in the industry, so I am looking for some advice on my particular situation. About me: Interested in creating intelligent robots. As cringy as it sounds, I do believe that star-wars-like intelligent robots are possible within my lifetime and my goal is to contribute towards that I love love love creating novel breakthrough technology that has the potential to improve everyone's lives. I like writing papers and creating tech in equal amounts. I love seeing the impact of my work firsthand. At this point in time, I don't think I'll enjoy an academic career because I like doing things with a more immediate impact. Thus I think I'd like to end up in industry research. Graduated MSc Rob…  ( 6 min )

Hi all. I need to implement a drone env to train neural network Capable of stabilizing a drone after throwing it. any suggestions for pre built envs or where to find informations on what i should consider if i want to build one on my own? I know how to use pybullet and the open ai gym interface so building one is not out of the question but a pre built one by a more experienced people would be better given the fact that I'm on tight schedule Sorry for my English not a native speaker :) submitted by /u/HerForFun998 [link] [comments]  ( 1 min )

Hi, I'm trying to implement unsupervised RL algorithms, like vision-based APT. I trained my agent and saw some meaningful behaviors in pre-training session. but, at finetune session, the agent showed bad adjustment. I guess over-estimation was occurred. (The experiment was action repeat 8 so it looks worse than official drq implementation.) Orange : scratch DrQ, gray : Finetuned version after APT 2M frames training Orange : scratch DrQ, gray : Finetuned version after APT 2M frames training How can I finetune that I had already pre-trained agent well? ​ Thanks for reading. submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 1 min )

Many machine learning tasks require high-quality data, such as assessing damage in a satellite that negatively affects a model’s performance. Datasets can cost millions of dollars to create if useable data exists, and even the best datasets sometimes contain biases that negatively impact a model’s performance. Many scientists have been working to answer an intriguing question working with synthetic data sampled from a generative model instead of real data. A generative model is a machine-learning model that requires significantly less memory to keep or share than a dataset. The range and quality of generative models have improved dramatically in recent years. Synthetic data has the ability to get around some of the privacy and usage rights problems that limit how actual data may be distributed. A generative model could potentially be updated to eliminate particular attributes, such as race or gender, to overcome biases in traditional datasets. New research by MIT Team develops a method for training a machine learning (ML) model that, rather than requiring a dataset, employs a particular form of ML model to generate exceptionally realistic synthetic data that can train another model for downstream vision tasks. Continue Reading Paper: https://openreview.net/pdf?id=qhAeZjs7dCL Github: https://github.com/ali-design/GenRep Project: https://ali-design.github.io/GenRep/ submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Is there any good ai for automating virtual characters in movies and games? submitted by /u/Ok-Suspect-9855 [link] [comments]

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Some one knows If there's a way for not geting disconected using the S2ML generator on Google colab? submitted by /u/Pale-Information3077 [link] [comments]

Seamless. Frictionless. Elegant. Efficient. Read More The post Building An Effective Experimentation Program – 01 Introduction appeared first on ML in Production.  ( 4 min )

Seamless. Frictionless. Elegant. Efficient. Read More The post Building An Effective Experimentation Program – 01 Introduction appeared first on ML in Production.  ( 4 min )

Hi! Does anyone know of any real-life applications of constrained optimization using newton's method in deep learning (specifically in minimizing the cost function)? Or if constrained optimization is even used at all when minimizing the cost function? I know that Newton's method can be used to find the minimum of the cost function in logistic regression. However, would there be any instances where we would add constraints to the cost function in logistic regression, therefore allowing us to use constrained optimization techniques with newton's method (such as Lagrange multipliers or interior-point methods). submitted by /u/No_Pop3856 [link] [comments]  ( 1 min )

With rising male infertility, sperm head morphology classification becomes critical for accurate and timely clinical diagnosis. Recent deep learning (DL) morphology analysis methods achieve promising benchmark results, but leave performance and robustness on the table by relying on limited and possibly noisy class labels. To address this, we introduce a new DL training framework that leverages anatomical and image priors from human sperm microscopy crops to extract useful features without additional labeling cost. Our core idea is to distill sperm head information with reliably-generated pseudo-masks and unsupervised spatial prediction tasks. The predicted foreground masks from this distillation step are then leveraged to regularize and reduce image and label noise in the tuning stage. We evaluate our new approach on two public sperm datasets and achieve state-of-the-art performances (e.g. 65.9% SCIAN accuracy and 96.5% HuSHeM accuracy).  ( 2 min )

Hi all, I recently read the YOLOR (You Only Learn One Representation) paper ( https://arxiv.org/abs/2105.04206 ). One of the key takeaways from this paper is that modelling implicit knowledge is crucial. And in order to make the model learn this implicit knowledge, simply "relaxing" the error term in cross-task learning is not enough, because then conventional loss functions won't be able to capture this implicit knowledge. So in order to tackle this problem, they model the error term for various tasks such that implicit knowledge can be learned. This reminded me very much of meta-RL, except I haven't seen any implementations of it where they explicitly model this per-task error and use a similar implicit+explicit knowledge learning framework. Since YOLOR is currently SOTA for realtime object detection and some other tasks, it seems like this was quite a significant insight and I wonder if anyone is aware of similar works in the "RL branch" of AI? I'd love to hear some thoughts about this or if someone can point me to related literature this would be great. In case this hasn't been looked into before, I'd love to start a PhD regarding this topic to learn more about it haha submitted by /u/Chronicle112 [link] [comments]  ( 1 min )

A bit of a rant here. I received a "major revision" recommendation for a paper I submitted a few months ago (was self-contained, no ML background was necessary). The field is mathematical and the journal also addresses numerical & computational aspects of it, but applying ML (especially neural net based L2 regressions) is still a bit unheard of there. As such, I made sure to dumb everything down for them and still gave the mathematical justifications, including convergence proofs. In short, it was about estimating certain conditional expectations having a very special structure, without resorting to heavy brute-force approaches (like nested Monte-Carlo), using L2 projections with neural networks and a special sampling scheme which was also explained in detail. In such an approach neural n…  ( 3 min )

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Hello everyone! I’ve recently read Swin Transformer paper and tried to implement with PyTorch. But there’re no post that FULLY explains the nitty-gritty details of the paper with full implementation. It took me soooo long time to write this post so I wanted to share with y’all! Hope this helps someone! The implementation is based on the official implementation of Microsoft team. https://jasonlee-cp.github.io/paper/Swin_Transformer/#swin-transformer-architecture submitted by /u/JasonTheCoders [link] [comments]  ( 1 min )

Hey, I write a lot of ML tooling and am hoping to present some of it in conferences somewhere. The main goal of publishing somewhere is to have a work that can be cited and is peer reviewed, but the real focus is on the package. Some people are weird about citing GitHub packages. Are there any specialized conferences for this sort of thing? Or do you just try your luck at a more niche conference? submitted by /u/puppet_pals [link] [comments]  ( 1 min )

https://github.com/akucia/analog-watch-recognition ​ https://preview.redd.it/4xrti7icgdo81.jpg?width=1200&format=pjpg&auto=webp&s=b76fd4aee01ef2eda4c9bb1c739d10ff38fe3cab submitted by /u/kuciu [link] [comments]

You can find the paper here: https://arxiv.org/abs/2201.11870 And the code and the data here: https://github.com/p-karisani/CEPC submitted by /u/payam_ka [link] [comments]

I am working on a hospital dataset, My Y variable is the total cost of the diagnosis. I wanna predict that so it's a regression problem, Ultimately. I used random forest and Dense NN Separately and I got pretty decent scores but is it possible to ensemble random forest and neural network. submitted by /u/nibras_28 [link] [comments]  ( 1 min )

I chatted with a few experts about Deepmind's recent work on using RL for Nuclear Fusion and Plasma Control. We collected some resources and took some notes to understand the work better. Thought to share them here: [video] Physics and Reinforcement Learning What is fusion? What is plasma? What is a Tokamak? Why do we care about conrolling plasma? The RL task is a continuous control task. That means that the inputs, “controller” take continuous, real values the same as would result from sliding a slider or turning a dial. Other examples of continuous control tasks are There are 19 of these metaphorical “dials” controlling coils in the tokamak. The task of the RL agent, then, is to learn how best to control those 19 “dials” given observations from the sensors within the tokamak.…  ( 5 min )

I started working through this paper on how neural networks divide and map their input space. It helped me visualize what happens in the hidden layers and how they help in better function approximation. I'm looking for some suggestions on papers to read which help me build a stronger intuition on how and why neural nets work. submitted by /u/theanswerisnt42 [link] [comments]  ( 2 min )

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I'm currently writing my Master's thesis on the merits and pitfalls of using reinforcement learning, specifically Proximal Policy Optimization, to finetune large language models. The project is inspired by the paper Fine-Tuning Language Models from Human Preferences, which uses a reward model trained on a dataset of human preferences to supply a reward signal used for fine-tuning GPT-2 to generate summaries and continuations that are optimized for human preference. I'm investigating the pros and cons of a more naive approach that does not require collecting a dataset of human preferences. Using the trl library, I train a BERT-classifier to distinguish between sarcastic and non-sarcastic reddit comments, and that classifier then serves as a reward model that provides a reward signal for fine-tuning GPT-2 for text generation using PPO. I have applied the same method to the task of generating negative review, by training BERT on the IMDB-dataset. This method of course leads to extensive reward hacking, but investigating how to mitigate that is part of the fun! I'm looking for inspiration for what other NLP tasks I could apply this to. The important constraint is that the tasks have to be something that I can train a classifier to evaluate, or otherwise evaluate algorithmically using metrics like ROUGE, because I need to supply GPT-2 with a scalar reward signal. What other tasks should I try this method out on? submitted by /u/Sisyfos42 [link] [comments]  ( 1 min )

I've been working with U-nets, and I’m curious about the possible extensions to improve the performance of U-nets. I found several so far: Variational U-nets: https://proceedings.neurips.cc/paper/2018/file/473447ac58e1cd7e96172575f48dca3b-Paper.pdf Unet++: https://arxiv.org/pdf/1912.05074.pdf Stacked Dilated U-nets: https://arxiv.org/pdf/2004.03466.pdf There seems to be more ways to expand on the traditional U-net architecture that I've yet to discover. I'd love to see other cool methods! submitted by /u/2133 [link] [comments]  ( 1 min )

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I'm a biologist, and I have a problem with a very common analysis done in my field. We often classify cells by unique profile proteins they express. Cells that are high in protein A but low in B may be called "Type 1", cells low in protein A but high in protein B may be called "Type 2", cells high in both A and B "Type 3", and cells low in both "Type 4". Sometimes this works well and cells are clearly one type or the other. But unfortunately nature doesn't care about our desire to neatly classify things, and I believe that cell identity exists on a spectrum. Protein expression isn't all or nothing, it's effectively a continuous variable. There are cases where some cells are probably actually "Type 1", some are actually "Type 2", but some meaningfully exist as "somewhere between Type 1 and…  ( 4 min )

Often-times data scientists work in silos. And later on in their project, they realize that “I wish I had known this ….” Some of the questions that arise are: ​ [Data] Training data is not representative of Production Data [Economics] Business had different success criteria than data scientist’s evaluation metrics [Engineering] Do not have an infrastructure in place to support models [Engineering] Product backend is written in Java and your pipeline is in Python [Legal] Need to take approval from Legal and Governance to consider data privacy, bias, and fairness [Stakeholders] Explainability is more important than results Have you come across any such situations? submitted by /u/rajg88 [link] [comments]  ( 1 min )

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Hi all! I am trying to use RL in order to train an agent that provides best direction to move in order to maximize the coverage of an area and I am giving reward on the basis of coverage increase at each step. I am implementing such algorithm both for 1 agent (so I am using simple DDPG) and for a set of agents (using MADDPG), but if I make the exploration noise decay, I noticed that the selected action of the model is always the same, independently on the input it receives. In fact, even during training, selected action is always a little update with respect to the one selected previously, even if the episode changes, and the inputs too. I have tried increasing/decresing the effect of noise (I am using Ornstein Uhlenbeck process) or decreasing the learning rates, but this seems to have no effect. What may be the problem of such a strange behaviour? submitted by /u/cosimobr [link] [comments]  ( 1 min )

I'm making DQN for automatic trading. Due to the nature of the problem, I figured the model would possibly need to be quite complex, but actually I have no idea just how big it should be. I am feeding it with 8000 numbers representing the last prices. Due to some reasons still unknown to me, I can basically add as many layers as I want as long as they are not bigger than 4096 neurons without increasing learning speed. I just wanted to ask: would 20 layers be too many ? Would 5 probably not be enough ? Right now the model looks like that: Dense(2048, "relu") Dense(1024, "relu") Dense(512, "relu") Dense(256, "relu") Dense(128, "relu") Dense(1024, "relu") Dense(2048, "relu") Dense(1024, "relu") Dense(512, "relu") Dense(256, "relu") Dense(128, "relu") Dense(64, "relu") Dense(32, "relu") Dense(16, "relu") Dense(8, "relu") Dense(3, "relu") Any help is appreciated, really I just want to have an idea of how much would be too much or too little for this specific problem, I'm a total beginner. submitted by /u/superpowers_fan [link] [comments]  ( 2 min )

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I am layman and just curious about the topic, how far are we to an AI which can understand the question and formulate a accurate and precise answer and not just return us the web results. submitted by /u/curiosityVeil [link] [comments]  ( 1 min )

I love technology but I do not like the increasing development of AI. I believe that AI has more dangers than positive impacts on humanity. submitted by /u/joginderMike [link] [comments]  ( 1 min )

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I attended an in-person customer event sponsored by Dataiku last week.  Very fun.  Man, do I miss the provocative and enlightening discussions that occur in these face-to-face customer engagements.  The icebreaker for fueling the dinner discussions was the following statement: “In the marketplace, dynamics in the job marketplace will evolve, and data-savvy subject matter experts… Read More »Is Data Scientist Weak Link in Data-drive Value Creation? The post Is Data Scientist Weak Link in Data-drive Value Creation? appeared first on Data Science Central.  ( 7 min )

submitted by /u/amin_mlm [link] [comments]  ( 1 min )

Hi everyone. I'm trying to debug my code and I can't manage to find what's wrong. I'm running a Roberta model with a classifier head on a Pytorch Lightning trainer and I just get stuck after it is running the optimizer config, it's loading the train data, calling __len__(from dataset) a couple of times, loading val data, calling len again a few times and then complete freeze, what could be going on? Also it's 100% running on the gpu but python is not using any gpu in task manager so I don't think it's actually doing anything. submitted by /u/DifferentMedia2536 [link] [comments]  ( 1 min )

This paper explores the threat detection for general Social Engineering (SE) attack using Machine Learning (ML) techniques, rather than focusing on or limited to a specific SE attack type, e.g. email phishing. Firstly, this paper processes and obtains more SE threat data from the previous Knowledge Graph (KG), and then extracts different threat features and generates new datasets corresponding with three different feature combinations. Finally, 9 types of ML models are created and trained using the three datasets, respectively, and their performance are compared and analyzed with 27 threat detectors and 270 times of experiments. The experimental results and analyses show that: 1) the ML techniques are feasible in detecting general SE attacks and some ML models are quite effective; ML-based SE threat detection is complementary with KG-based approaches; 2) the generated datasets are usable and the SE domain ontology proposed in previous work can dissect SE attacks and deliver the SE threat features, allowing it to be used as a data model for future research. Besides, more conclusions and analyses about the characteristics of different ML detectors and the datasets are discussed.  ( 2 min )

As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances. As the real-world is an open environment that is full of unknowns or novelties, detecting novelties, characterizing them, accommodating or adapting to them, and gathering ground-truth training data and incrementally learning the unknowns/novelties are critical to making the AI agent more and more knowledgeable and powerful over time. The key challenge is how to automate the process so that it is carried out continually on the agent's own initiative and through its own interactions with humans, other agents and the environment just like human on-the-job learning. This paper proposes a framework (called SOLA) for this learning paradigm to promote the research of building autonomous and continual learning enabled AI agents. To show feasibility, an implemented agent is also described.  ( 2 min )

Federated learning is an established method for training machine learning models without sharing training data. However, recent work has shown that it cannot guarantee data privacy as shared gradients can still leak sensitive information. To formalize the problem of gradient leakage, we propose a theoretical framework that enables, for the first time, analysis of the Bayes optimal adversary phrased as an optimization problem. We demonstrate that existing leakage attacks can be seen as approximations of this optimal adversary with different assumptions on the probability distributions of the input data and gradients. Our experiments confirm the effectiveness of the Bayes optimal adversary when it has knowledge of the underlying distribution. Further, our experimental evaluation shows that several existing heuristic defenses are not effective against stronger attacks, especially early in the training process. Thus, our findings indicate that the construction of more effective defenses and their evaluation remains an open problem.  ( 2 min )

Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. Further, we show extra improvements when coupling it with existing fine-tuning techniques at the cost of increased running time. Finally, we propose a novel and fast fine-tuning model that adjusts torsion angles of a ligand's rotatable bonds based on closed-form global minima of the von Mises angular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization.  ( 2 min )

Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is utilized in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each direction's weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, $k$-means clustering, and $k$-nearest neighbor classification. When the distance is estimated from an unlabeled dataset, we establish the upper bound on distance estimation's accuracy and the number of samples sufficient for downstream task improvement. Finally, numerical experiments are presented to support the theoretical results in the paper.  ( 2 min )

We introduce Backpropagation Through Time and Space (BPTTS), a method for training a recurrent spatio-temporal neural network, that is used in a homogeneous multi-agent reinforcement learning (MARL) setting to learn numerical methods for hyperbolic conservation laws. We treat the numerical schemes underlying partial differential equations (PDEs) as a Partially Observable Markov Game (POMG) in Reinforcement Learning (RL). Similar to numerical solvers, our agent acts at each discrete location of a computational space for efficient and generalizable learning. To learn higher-order spatial methods by acting on local states, the agent must discern how its actions at a given spatiotemporal location affect the future evolution of the state. The manifestation of this non-stationarity is addressed by BPTTS, which allows for the flow of gradients across both space and time. The learned numerical policies are comparable to the SOTA numerics in two settings, the Burgers' Equation and the Euler Equations, and generalize well to other simulation set-ups.  ( 2 min )

We study the performance of different pool-based Batch Mode Deep Active Learning (BMDAL) methods for regression on tabular data, focusing on methods that do not require to modify the network architecture and training. Our contributions are three-fold: First, we present a framework for constructing BMDAL methods out of kernels, kernel transformations and selection methods, showing that many of the most popular BMDAL methods fit into our framework. Second, we propose new components, leading to a new BMDAL method. Third, we introduce an open-source benchmark with 15 large tabular data sets, which we use to compare different BMDAL methods. Our benchmark results show that a combination of our novel components yields new state-of-the-art results in terms of RMSE and is computationally efficient. We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.  ( 2 min )

In probably approximately correct (PAC) reinforcement learning (RL), an agent is required to identify an $\epsilon$-optimal policy with probability $1-\delta$. While minimax optimal algorithms exist for this problem, its instance-dependent complexity remains elusive in episodic Markov decision processes (MDPs). In this paper, we propose the first (nearly) matching upper and lower bounds on the sample complexity of PAC RL in deterministic episodic MDPs with finite state and action spaces. In particular, our bounds feature a new notion of sub-optimality gap for state-action pairs that we call the deterministic return gap. While our instance-dependent lower bound is written as a linear program, our algorithms are very simple and do not require solving such an optimization problem during learning. Their design and analyses employ novel ideas, including graph-theoretical concepts such as minimum flows and maximum cuts, which we believe to shed new light on this problem.  ( 2 min )

Unsupervised audio-visual source localization aims at localizing visible sound sources in a video without relying on ground-truth localization for training. Previous works often seek high audio-visual similarities for likely positive (sounding) regions and low similarities for likely negative regions. However, accurately distinguishing between sounding and non-sounding regions is challenging without manual annotations. In this work, we propose a simple yet effective approach for Easy Visual Sound Localization, namely EZ-VSL, without relying on the construction of positive and/or negative regions during training. Instead, we align audio and visual spaces by seeking audio-visual representations that are aligned in, at least, one location of the associated image, while not matching other images, at any location. We also introduce a novel object guided localization scheme at inference time for improved precision. Our simple and effective framework achieves state-of-the-art performance on two popular benchmarks, Flickr SoundNet and VGG-Sound Source. In particular, we improve the CIoU of the Flickr SoundNet test set from 76.80% to 83.94%, and on the VGG-Sound Source dataset from 34.60% to 38.85%. The code is available at https://github.com/stoneMo/EZ-VSL.  ( 2 min )

In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, for communication-efficiency distributed Deep Neural Network (DNN) training. $\mathsf{CO}_3$ is a joint training/communication protocol, which encompasses three processing steps for the network gradients: (i) quantization through floating-point conversion, (ii) lossless compression, and (iii) error correction. These three components are crucial in the implementation of distributed DNN training over rate-constrained links. The interplay of these three steps in processing the DNN gradients is carefully balanced to yield a robust and high-performance scheme. The performance of the proposed scheme is investigated through numerical evaluations over CIFAR-10.  ( 2 min )

Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to future outcomes. In this paper, we consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to an extreme delay of reward signals, in which the agent can only obtain one reward signal at the end of each trajectory. A popular paradigm for this problem setting is learning with a designed auxiliary dense reward function, namely proxy reward, instead of sparse environmental signals. Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning. We establish a surrogate problem by Monte-Carlo sampling that scales up least-squares-based reward redistribution to long-horizon problems. We analyze our surrogate loss function by connection with existing methods in the literature, which illustrates the algorithmic properties of our approach. In experiments, we extensively evaluate our proposed method on a variety of benchmark tasks with episodic rewards and demonstrate substantial improvement over baseline algorithms.  ( 2 min )

Nowadays, our mobility systems are evolving into the era of intelligent vehicles that aim to improve road safety. Due to their vulnerability, pedestrians are the users who will benefit the most from these developments. However, predicting their trajectory is one of the most challenging concerns. Indeed, accurate prediction requires a good understanding of multi-agent interactions that can be complex. Learning the underlying spatial and temporal patterns caused by these interactions is even more of a competitive and open problem that many researchers are tackling. In this paper, we introduce a model called PRediction Transformer (PReTR) that extracts features from the multi-agent scenes by employing a factorized spatio-temporal attention module. It shows less computational needs than previously studied models with empirically better results. Besides, previous works in motion prediction suffer from the exposure bias problem caused by generating future sequences conditioned on model prediction samples rather than ground-truth samples. In order to go beyond the proposed solutions, we leverage encoder-decoder Transformer networks for parallel decoding a set of learned object queries. This non-autoregressive solution avoids the need for iterative conditioning and arguably decreases training and testing computational time. We evaluate our model on the ETH/UCY datasets, a publicly available benchmark for pedestrian trajectory prediction. Finally, we justify our usage of the parallel decoding technique by showing that the trajectory prediction task can be better solved as a non-autoregressive task.  ( 2 min )

There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT and T5 on NLU tasks. Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.  ( 2 min )

We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we derive a continuous-time model for the dynamics of the data via the finite element method. The resulting graph neural network estimates the instantaneous effects of the unknown dynamics on each cell in a meshing of the spatial domain. Our model can incorporate prior knowledge via assumptions on the form of the unknown PDE, which induce a structural bias towards learning specific processes. Through this mechanism, we derive a transport variant of our model from the convection equation and show that it improves the transfer performance to higher-resolution meshes on sea surface temperature and gas flow forecasting against baseline models representing a selection of spatio-temporal forecasting methods. A qualitative analysis shows that our model disentangles the data dynamics into their constituent parts, which makes it uniquely interpretable.  ( 2 min )

As deep learning becomes the mainstream in the field of natural language processing, the need for suitable active learning method are becoming unprecedented urgent. Active Learning (AL) methods based on nearest neighbor classifier are proposed and demonstrated superior results. However, existing nearest neighbor classifier are not suitable for classifying mutual exclusive classes because inter-class discrepancy cannot be assured by nearest neighbor classifiers. As a result, informative samples in the margin area can not be discovered and AL performance are damaged. To this end, we propose a novel Nearest neighbor Classifier with Margin penalty for Active Learning(NCMAL). Firstly, mandatory margin penalty are added between classes, therefore both inter-class discrepancy and intra-class compactness are both assured. Secondly, a novel sample selection strategy are proposed to discover informative samples within the margin area. To demonstrate the effectiveness of the methods, we conduct extensive experiments on for datasets with other state-of-the-art methods. The experimental results demonstrate that our method achieves better results with fewer annotated samples than all baseline methods.  ( 2 min )

Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the parameters of a heteroscedastic Gaussian distribution by maximizing the logarithm of the likelihood function under the observed data. In this work, we examine this approach and identify potential hazards associated with the use of log-likelihood in conjunction with gradient-based optimizers. First, we present a synthetic example illustrating how this approach can lead to very poor but stable parameter estimates. Second, we identify the culprit to be the log-likelihood loss, along with certain conditions that exacerbate the issue. Third, we present an alternative formulation, termed $\beta$-NLL, in which each data point's contribution to the loss is weighted by the $\beta$-exponentiated variance estimate. We show that using an appropriate $\beta$ largely mitigates the issue in our illustrative example. Fourth, we evaluate this approach on a range of domains and tasks and show that it achieves considerable improvements and performs more robustly concerning hyperparameters, both in predictive RMSE and log-likelihood criteria.  ( 2 min )

We introduce the model selection problem in pure exploration linear bandits, where the learner needs to adapt to the instance-dependent complexity measure of the smallest hypothesis class containing the true model. We design algorithms in both fixed confidence and fixed budget settings with near instance optimal guarantees. The core of our algorithms is a new optimization problem based on experimental design that leverages the geometry of the action set to identify a near-optimal hypothesis class. Our fixed budget algorithm is developed based on a novel selection-validation procedure, which provides a new way to study the understudied fixed budget setting (even without the added challenge of model selection). We adapt our algorithms, in both fixed confidence and fixed budget settings, to problems with model misspecification.  ( 2 min )

Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a hard sample mining scheme to achieve effective knowledge distillation throughout the training. In addition, we develop customized label sampling and class-level ensemble to derive maximum utilization of knowledge, which implicitly mitigates the distribution discrepancy across clients. Extensive experiments show that our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.  ( 2 min )

Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of keeping around an infinite supply of older (less relevant) data. In addition, we argue that increasing the stock of data by including older datasets may, in fact, damage the model's accuracy. Expectedly, the model's accuracy improves by increasing the flow of data (defined as data collection rate); however, it requires other tradeoffs in terms of refreshing or retraining machine learning models more frequently. Using these results, we investigate how the business value created by machine learning models scales with data and when the stock of data establishes a sustainable competitive advantage. We argue that data's time-dependency weakens the barrier to entry that the stock of data creates. As a result, a competing firm equipped with a limited (yet sufficient) amount of recent data can develop more accurate models. This result, coupled with the fact that older datasets may deteriorate models' accuracy, suggests that created business value doesn't scale with the stock of available data unless the firm offloads less relevant data from its data repository. Consequently, a firm's growth policy should incorporate a balance between the stock of historical data and the flow of new data. We complement our theoretical results with an experiment. In the experiment, we empirically measure the loss in the accuracy of a next word prediction model trained on datasets from various time periods. Our empirical measurements confirm the economic significance of the value decline over time. For example, 100MB of text data, after seven years, becomes as valuable as 50MB of current data for the next word prediction task.  ( 2 min )

Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain. We address these issues for a particularly pervasive type of domain shift called measurement shift which can be resolved by restoring the source features rather than extracting new ones. In particular, we propose Feature Restoration (FR) wherein we: (i) store a lightweight and flexible approximation of the feature distribution under the source data; and (ii) adapt the feature-extractor such that the approximate feature distribution under the target data realigns with that saved on the source. We additionally propose a bottom-up training scheme which boosts performance, which we call Bottom-Up Feature Restoration (BUFR). On real and synthetic data, we demonstrate that BUFR outperforms existing SFDA methods in terms of accuracy, calibration, and data efficiency, while being less reliant on the performance of the source model in the target domain.  ( 2 min )

Objective: Disease knowledge graphs are a way to connect, organize, and access disparate information about diseases with numerous benefits for artificial intelligence (AI). To create knowledge graphs, it is necessary to extract knowledge from multimodal datasets in the form of relationships between disease concepts and normalize both concepts and relationship types. Methods: We introduce REMAP, a multimodal approach for disease relation extraction and classification. The REMAP machine learning approach jointly embeds a partial, incomplete knowledge graph and a medical language dataset into a compact latent vector space, followed by aligning the multimodal embeddings for optimal disease relation extraction. Results: We apply REMAP approach to a disease knowledge graph with 96,913 relations and a text dataset of 1.24 million sentences. On a dataset annotated by human experts, REMAP improves text-based disease relation extraction by 10.0% (accuracy) and 17.2% (F1-score) by fusing disease knowledge graphs with text information. Further, REMAP leverages text information to recommend new relationships in the knowledge graph, outperforming graph-based methods by 8.4% (accuracy) and 10.4% (F1-score). Discussion: Systematized knowledge is becoming the backbone of AI, creating opportunities to inject semantics into AI and fully integrate it into machine learning algorithms. While prior semantic knowledge can assist in extracting disease relationships from text, existing methods can not fully leverage multimodal datasets. Conclusion: REMAP is a multimodal approach for extracting and classifying disease relationships by fusing structured knowledge and text information. REMAP provides a flexible neural architecture to easily find, access, and validate AI-driven relationships between disease concepts.  ( 2 min )

In this paper, we propose an optimal rejection method for rejecting ambiguous samples by a rejection function. This rejection function is trained together with a classification function under the framework of Learning-with-Rejection (LwR). The highlights of LwR are: (1) the rejection strategy is not heuristic but has a strong background from a machine learning theory, and (2) the rejection function can be trained on an arbitrary feature space which is different from the feature space for classification. The latter suggests we can choose a feature space that is more suitable for rejection. Although the past research on LwR focused only on its theoretical aspect, we propose to utilize LwR for practical pattern classification tasks. Moreover, we propose to use features from different CNN layers for classification and rejection. Our extensive experiments of notMNIST classification and character/non-character classification demonstrate that the proposed method achieves better performance than traditional rejection strategies.  ( 2 min )

Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respect to the circuit depth and the qubit number. This result leads to a general belief that deep quantum circuits will not be feasible for practical tasks. In this work, we propose an initialization strategy with theoretical guarantees for the vanishing gradient problem in general deep circuits. Specifically, we prove that under proper Gaussian initialized parameters, the norm of the gradient decays at most polynomially when the qubit number and the circuit depth increase. Our theoretical results hold for both the local and the global observable cases, where the latter was believed to have vanishing gradients even for shallow circuits. Experimental results verify our theoretical findings in the quantum simulation and quantum chemistry.  ( 2 min )

Data Augmentation (DA) is known to improve the generalizability of deep neural networks. Most existing DA techniques naively add a certain number of augmented samples without considering the quality and the added computational cost of these samples. To tackle this problem, a common strategy, adopted by several state-of-the-art DA methods, is to adaptively generate or re-weight augmented samples with respect to the task objective during training. However, these adaptive DA methods: (1) are computationally expensive and not sample-efficient, and (2) are designed merely for a specific setting. In this work, we present a universal DA technique, called Glitter, to overcome both issues. Glitter can be plugged into any DA method, making training sample-efficient without sacrificing performance. From a pre-generated pool of augmented samples, Glitter adaptively selects a subset of worst-case samples with maximal loss, analogous to adversarial DA. Without altering the training strategy, the task objective can be optimized on the selected subset. Our thorough experiments on the GLUE benchmark, SQuAD, and HellaSwag in three widely used training setups including consistency training, self-distillation and knowledge distillation reveal that Glitter is substantially faster to train and achieves a competitive performance, compared to strong baselines.  ( 2 min )

In this work, we study the applications of differential privacy (DP) in the context of graph-structured data. We discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). The formulation of DP in the context of graph-structured data is difficult, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. This issue extends to the domain of GNNs, rendering private machine learning on graph-structured data a challenging task. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, discuss challenges and promising applications, including the GNN domain. We compare and separate works into graph analysis tasks and graph learning tasks with GNNs. Finally, we conclude our work with a discussion of open questions and potential directions for further research in this area.  ( 2 min )

Speaker embedding is an important front-end module to explore discriminative speaker features for many speech applications where speaker information is needed. Current SOTA backbone networks for speaker embedding are designed to aggregate multi-scale features from an utterance with multi-branch network architectures for speaker representation. However, naively adding many branches of multi-scale features with the simple fully convolutional operation could not efficiently improve the performance due to the rapid increase of model parameters and computational complexity. Therefore, in the most current state-of-the-art network architectures, only a few branches corresponding to a limited number of temporal scales could be designed for speaker embeddings. To address this problem, in this paper, we propose an effective temporal multi-scale (TMS) model where multi-scale branches could be efficiently designed in a speaker embedding network almost without increasing computational costs. The new model is based on the conventional TDNN, where the network architecture is smartly separated into two modeling operators: a channel-modeling operator and a temporal multi-branch modeling operator. Adding temporal multi-scale in the temporal multi-branch operator needs only a little bit increase of the number of parameters, and thus save more computational budget for adding more branches with large temporal scales. Moreover, in the inference stage, we further developed a systemic re-parameterization method to convert the TMS-based model into a single-path-based topology in order to increase inference speed. We investigated the performance of the new TMS method for automatic speaker verification (ASV) on in-domain and out-of-domain conditions. Results show that the TMS-based model obtained a significant increase in the performance over the SOTA ASV models, meanwhile, had a faster inference speed.  ( 2 min )

Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing (semi-)supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming. In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge. There have been many studies working on FSLG recently. In this paper, we comprehensively survey these work in the form of a series of methods and applications. Specifically, we first introduce FSLG challenges and bases, then categorize and summarize existing work of FSLG in terms of three major graph mining tasks at different granularity levels, i.e., node, edge, and graph. Finally, we share our thoughts on some future research directions of FSLG. The authors of this survey have contributed significantly to the AI literature on FSLG over the last few years.  ( 2 min )

Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the underlying reasons for the effectiveness of these GAL methods are still unclear. As a consequence, how to choose optimal graph augmentation strategy for a certain application scenario is still in black box. There is a lack of systematic, comprehensive, and experimentally validated guideline of GAL for scholars. Therefore, in this survey, we in-depth review GAL techniques from macro (graph), meso (subgraph), and micro (node/edge) levels. We further detailedly illustrate how GAL enhance the data quality and the model performance. The aggregation mechanism of augmentation strategies and graph learning models are also discussed by different application scenarios, i.e., data-specific, model-specific, and hybrid scenarios. To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks. Finally, we share our insights on several open issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability, and generalization.  ( 2 min )

Medical image segmentation has been so far achieving promising results with Convolutional Neural Networks (CNNs). However, it is arguable that in traditional CNNs, its pooling layer tends to discard important information such as positions. Moreover, CNNs are sensitive to rotation and affine transformation. Capsule network is a data-efficient network design proposed to overcome such limitations by replacing pooling layers with dynamic routing and convolutional strides, which aims to preserve the part-whole relationships. Capsule network has shown a great performance in image recognition and natural language processing, but applications for medical image segmentation, particularly volumetric image segmentation, has been limited. In this work, we propose 3D-UCaps, a 3D voxel-based Capsule network for medical volumetric image segmentation. We build the concept of capsules into a CNN by designing a network with two pathways: the first pathway is encoded by 3D Capsule blocks, whereas the second pathway is decoded by 3D CNNs blocks. 3D-UCaps, therefore inherits the merits from both Capsule network to preserve the spatial relationship and CNNs to learn visual representation. We conducted experiments on various datasets to demonstrate the robustness of 3D-UCaps including iSeg-2017, LUNA16, Hippocampus, and Cardiac, where our method outperforms previous Capsule networks and 3D-Unets.  ( 2 min )

Recent methods for embodied instruction following are typically trained end-to-end using imitation learning. This often requires the use of expert trajectories and low-level language instructions. Such approaches assume that neural states will integrate multimodal semantics to perform state tracking, building spatial memory, exploration, and long-term planning. In contrast, we propose a modular method with structured representations that (1) builds a semantic map of the scene and (2) performs exploration with a semantic search policy, to achieve the natural language goal. Our modular method achieves SOTA performance (24.46 %) with a substantial (8.17 % absolute) gap from previous work while using less data by eschewing both expert trajectories and low-level instructions. Leveraging low-level language, however, can further increase our performance (26.49 %). Our findings suggest that an explicit spatial memory and a semantic search policy can provide a stronger and more general representation for state-tracking and guidance, even in the absence of expert trajectories or low-level instructions.  ( 2 min )

Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a backdoor is inserted into the neural network by poisoning the training dataset, misclassifying inputs that contain the adversary trigger. The major challenge for defending against these attacks is that only the attacker knows the secret trigger and the target class. The problem is further exacerbated by the recent introduction of "Hidden Triggers", where the triggers are carefully fused into the input, bypassing detection by human inspection and causing backdoor identification through anomaly detection to fail. To defend against such imperceptible attacks, in this work we systematically analyze how representations, i.e., the set of neuron activations for a given DNN when using the training data as inputs, are affected by backdoor attacks. We propose PiDAn, an algorithm based on coherence optimization purifying the poisoned data. Our analysis shows that representations of poisoned data and authentic data in the target class are still embedded in different linear subspaces, which implies that they show different coherence with some latent spaces. Based on this observation, the proposed PiDAn algorithm learns a sample-wise weight vector to maximize the projected coherence of weighted samples, where we demonstrate that the learned weight vector has a natural "grouping effect" and is distinguishable between authentic data and poisoned data. This enables the systematic detection and mitigation of backdoor attacks. Based on our theoretical analysis and experimental results, we demonstrate the effectiveness of PiDAn in defending against backdoor attacks that use different settings of poisoned samples on GTSRB and ILSVRC2012 datasets. Our PiDAn algorithm can detect more than 90% infected classes and identify 95% poisoned samples.  ( 2 min )

Here, we report measurements of detailed dynamic cohesive properties (DCPs) beyond the dynamic fracture toughness of a bicontinuously nanostructured copolymer, polyurea, under an extremely loading rate, from deep-learning analyses of a dynamic big-data-generating experiment. We first describe a new Dynamic Line-Image Shearing Interferometer (DL-ISI), which uses a streak camera to record optical fringes of displacement-gradient vs time profile along a line on sample's rear surface. This system enables us to detect crack initiation and growth processes in plate-impact experiments. Then, we present a convolutional neural network (CNN) based deep-learning framework, trained by extensive finite-element simulations, that inversely determines the accurate DCPs from the DL-ISI fringe images. For the measurements, plate-impact experiments were performed on a set of samples with a mid-plane crack. A Conditional Generative Adversarial Networks (cGAN) was employed first to reconstruct missing DL-ISI fringes with recorded partial DL-ISI fringes. Then, the CNN and a correlation method were applied to the fully reconstructed fringes to get the dynamic fracture toughness, 12.1kJ/m^2, cohesive strength, 302 MPa, and maximum cohesive separation, 80.5 um, within 0.4%, 2.7%, and 2.2% differences, respectively. For the first time, the DCPs of polyurea have been successfully obtained by the DL-ISI with the pre-trained CNN and correlation analyses of cGAN-reconstructed data sets. The dynamic cohesive strength is found to be nearly three times higher than the dynamic-failure-initiation strength. The high dynamic fracture toughness is found to stem from both high dynamic cohesive strength and high ductility of the dynamic cohesive separation.  ( 2 min )

We present a general convergent class of reinforcement learning algorithms that is founded on two distinct principles: (1) mapping value estimates to a different space using arbitrary functions from a broad class, and (2) linearly decomposing the reward signal into multiple channels. The first principle enables incorporating specific properties into the value estimator that can enhance learning. The second principle, on the other hand, allows for the value function to be represented as a composition of multiple utility functions. This can be leveraged for various purposes, e.g. dealing with highly varying reward scales, incorporating a priori knowledge about the sources of reward, and ensemble learning. Combining the two principles yields a general blueprint for instantiating convergent algorithms by orchestrating diverse mapping functions over multiple reward channels. This blueprint generalizes and subsumes algorithms such as Q-Learning, Log Q-Learning, and Q-Decomposition. In addition, our convergence proof for this general class relaxes certain required assumptions in some of these algorithms. Based on our theory, we discuss several interesting configurations as special cases. Finally, to illustrate the potential of the design space that our theory opens up, we instantiate a particular algorithm and evaluate its performance on the Atari suite.  ( 2 min )

Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts, explanations of the foundational principles underpinning the effectiveness of Adversarial Training are limited and far from being widely accepted by the Deep Learning community. In this paper, we describe surprising properties of adversarially-trained models, shedding light on mechanisms through which robustness against adversarial attacks is implemented. Moreover, we highlight limitations and failure modes affecting these models that were not discussed by prior works. We conduct extensive analyses on a wide range of architectures and datasets, performing a deep comparison between robust and natural models.  ( 2 min )

The placement of temperature sensitive and safety-critical components is crucial in the automotive industry. It is therefore inevitable, even at the design stage of new vehicles that these components are assessed for potential safety issues. However, with increasing number of design proposals, risk assessment quickly becomes expensive. We therefore present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations. The proposed physics-informed neural network (PINN) design is aimed at learning families of flow solutions according to a geometric variation. In scope of this work, we could show that our nondimensional, multivariate scheme can be efficiently trained to predict the velocity and pressure distribution for different design scenarios and geometric scales. The proposed algorithm is based on a parametric minibatch training which enables the utilization of large datasets necessary for the three-dimensional flow modeling. Further, we introduce a continuous resampling algorithm that allows to operate on one static dataset. Every feature of our methodology is tested individually and verified against conventional CFD simulations. Finally, we apply our proposed method in context of an exemplary real-world automotive application.  ( 2 min )

The abundance of dark matter subhalos orbiting a host galaxy is a generic prediction of the cosmological framework. It is a promising way to constrain the nature of dark matter. Here we describe the challenges of detecting stars whose phase-space distribution may be perturbed by the passage of dark matter subhalos using a machine learning approach. The training data are three Milky Way-like galaxies and nine synthetic Gaia DR2 surveys derived from these. We first quantify the magnitude of the perturbations in the simulated galaxies using an anomaly detection algorithm. We also estimate the feasibility of this approach in the Gaia DR2-like catalogues by comparing the anomaly detection based approach with a supervised classification. We find that a classification algorithm optimized on about half a billion synthetic star observables exhibits mild but nonzero sensitivity. This classification-based approach is not sufficiently sensitive to pinpoint the exact locations of subhalos in the simulation, as would be expected from the very limited number of subhalos in the detectable region. The enormous size of the Gaia dataset motivates the further development of scalable and accurate computational methods that could be used to select potential regions of interest for dark matter searches to ultimately constrain the Milky Way's subhalo mass function.  ( 2 min )

Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models' pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely held-out tasks. We fine-tune a pretrained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, our approach attains strong performance on a subset of tasks from the BIG-bench benchmark, outperforming models up to 6x its size. All trained models are available at https://github.com/bigscience-workshop/t-zero and all prompts are available at https://github.com/bigscience-workshop/promptsource.  ( 3 min )

The laborious process of labeling data often bottlenecks projects that aim to leverage the power of supervised machine learning. Active Learning (AL) has been established as a technique to ameliorate this condition through an iterative framework that queries a human annotator for labels of instances with the most uncertain class assignment. Via this mechanism, AL produces a binary classifier trained on less labeled data but with little, if any, loss in predictive performance. Despite its advantages, AL can have difficulty with class-imbalanced datasets and results in an inefficient labeling process. To address these drawbacks, we investigate our unsupervised instance selection (UNISEL) technique followed by a Random Forest (RF) classifier on 10 outlier detection datasets under low-label conditions. These results are compared to AL performed on the same datasets. Further, we investigate the combination of UNISEL and AL. Results indicate that UNISEL followed by an RF performs comparably to AL with an RF and that the combination of UNISEL and AL demonstrates superior performance. The practical implications of these findings in terms of time savings and generalizability afforded by UNISEL are discussed.  ( 2 min )

Reinforcement learning (RL) tasks are typically framed as Markov Decision Processes (MDPs), assuming that decisions are made at fixed time intervals. However, many applications of great importance, including healthcare, do not satisfy this assumption, yet they are commonly modelled as MDPs after an artificial reshaping of the data. In addition, most healthcare (and similar) problems are offline by nature, allowing for only retrospective studies. To address both challenges, we begin by discussing the Semi-MDP (SMDP) framework, which formally handles actions of variable timings. We next present a formal way to apply SMDP modifications to nearly any given value-based offline RL method. We use this theory to introduce three SMDP-based offline RL algorithms, namely, SDQN, SDDQN, and SBCQ. We then experimentally demonstrate that these SMDP-based algorithms learn the optimal policy in these variable-time environments, whereas un-directed modifications of MDP modelling lead to sub-optimal policies. Finally, we apply our new algorithms to a real-world offline dataset pertaining to warfarin dosing for stroke prevention and demonstrate similar results.  ( 2 min )

We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss of information. We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary. PROSUB uses the paradigm of recursive feature elimination (RFE) and progressively subsamples measurements during deep learning training, improving optimization stability. PROSUB also integrates a neural architecture search (NAS) paradigm, allowing the network architecture hyperparameters to respond to the subsampling process. We show PROSUB outperforms the winner of the MUDI MICCAI challenge, producing large improvements >18% MSE on the MUDI challenge sub-tasks and qualitative improvements on downstream processes useful for clinical applications. We also show the benefits of incorporating NAS and analyze the effect of PROSUB's components. As our method generalizes to other problems beyond MRI measurement selection-reconstruction, our code is https://github.com/sbb-gh/PROSUB  ( 2 min )

We develop a method to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL), using on-policy algorithms that are more suitable to learn from pre-trained networks. We show that the combination of IL and RL can match human results and that good performance strongly depends on an egocentric representation of the environment. The developed methodology can be used to efficiently learn policies for unmanned vehicles which have to solve missions in an open field environment.  ( 2 min )

Here, we present a Heterogeneous Graph neural network for Multimodal neuroimaging fusion learning (HGM). Traditional GNN-based models usually assume the brain network is a homogeneous graph with single type of nodes and edges. However, vast literatures have shown the heterogeneity of the human brain especially between the two hemispheres. Homogeneous brain network is insufficient to model the complicated brain state. Therefore, in this work we firstly model the brain network as heterogeneous graph with multi-type nodes (i.e., left and right hemispheric nodes) and multi-type edges (i.e., intra- and inter-hemispheric edges). Besides, we also propose a self-supervised pre-training strategy based on heterogeneou brain network to address the overfitting problem due to the complex model and small sample size. Our results on two datasets show the superiority of proposed model over other multimodal methods for disease prediction task. Besides, ablation experiments show that our model with pre-training strategy can alleviate the problem of limited training sample size.  ( 2 min )

Graph Neural Networks (GNNs) have emerged as prominent models for representation learning on graph structured data. GNNs follow an approach of message passing analogous to 1-dimensional Weisfeiler Lehman (1-WL) test for graph isomorphism and consequently are limited by the distinguishing power of 1-WL. More expressive higher-order GNNs which operate on k-tuples of nodes need increased computational resources in order to process higher-order tensors. Instead of the WL approach, in this work, we follow the classical approach of Individualization and Refinement (IR), a technique followed by most practical isomorphism solvers. Individualization refers to artificially distinguishing a node in the graph and refinement is the propagation of this information to other nodes through message passing. We learn to adaptively select nodes to individualize and to aggregate the resulting graphs after refinement to help handle the complexity. Our technique lets us learn richer node embeddings while keeping the computational complexity manageable. Theoretically, we show that our procedure is more expressive than the 1-WL test. Experiments show that our method outperforms prominent 1-WL GNN models as well as competitive higher-order baselines on several benchmark synthetic and real datasets. Furthermore, our method opens new doors for exploring the paradigm of learning on graph structures with individualization and refinement.  ( 2 min )

Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial perturbations present, while the convergence of IBP training remains unknown in existing literature. In this paper, we present a theoretical analysis on the convergence of IBP training. With an overparameterized assumption, we analyze the convergence of IBP robust training. We show that when using IBP training to train a randomly initialized two-layer ReLU neural network with logistic loss, gradient descent can linearly converge to zero robust training error with a high probability if we have sufficiently small perturbation radius and large network width.

Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizons, and must be trained sequentially. Convolutional networks are unable to handle sequences of unknown size and their memory horizon must be defined a priori. In this work, we show that all these problems can be solved by formulating convolutional kernels in CNNs as continuous functions. The resulting Continuous Kernel Convolution (CKConv) allows us to model arbitrarily long sequences in a parallel manner, within a single operation, and without relying on any form of recurrence. We show that Continuous Kernel Convolutional Networks (CKCNNs) obtain state-of-the-art results in multiple datasets, e.g., permuted MNIST, and, thanks to their continuous nature, are able to handle non-uniformly sampled datasets and irregularly-sampled data natively. CKCNNs match or perform better than neural ODEs designed for these purposes in a faster and simpler manner.  ( 2 min )

Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisions are crucial. Therefore, we aim to extend the defect detection task towards an interactive human-in-the-loop approach that allows us to integrate rich background knowledge and the inference of complex relationships going beyond traditional purely data-driven approaches. We propose an approach for an interactive support system for classifications in an industrial quality control setting that combines the advantages of both (explainable) knowledge-driven and data-driven machine learning methods, in particular inductive logic programming and convolutional neural networks, with human expertise and control. The resulting system can assist domain experts with decisions, provide transparent explanations for results, and integrate feedback from users; thus reducing workload for humans while both respecting their expertise and without removing their agency or accountability.  ( 2 min )

As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models are limited in their ability to explain dynamic state changes in the brain which occurs spontaneously as a result of normal brain function. Hidden Markov Models (HMMs) trained on neuroimaging time series data have since arisen as a method to produce dynamical models that are easy to train but can be difficult to fully parametrise or analyse. We propose an interpretation of these neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models (HMGMs). This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. Furthermore, we propose a general method for selecting HMM hyperparameters in the absence of external data, based on the principle of maximum entropy, and use this to select the number of layers in the multiplex model. We produce a new tool for determining important communities of brain regions using a spatiotemporal random walk-based procedure that takes advantage of the underlying Markov structure of the model. Our analysis of real multi-subject fMRI data provides new results that corroborate the modular processing hypothesis of the brain at rest as well as contributing new evidence of functional overlap between and within dynamic brain state communities. Our analysis pipeline provides a way to characterise dynamic network activity of the brain under novel behaviours or conditions.  ( 3 min )

This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality. The cells we compare include classical long short term memory (LSTM), gated recurrent unit (GRU), modified LSTM with dilation, and two new cells we proposed recently, which are equipped with dilation and attention mechanisms. To model the temporal dependencies of different scales, our RNN architecture has multiple dilated recurrent layers stacked with hierarchical dilations. The proposed RNN produces both point forecasts and predictive intervals (PIs) for them. An empirical study concerning short-term electrical load forecasting for 35 European countries confirmed that the new gated cells with dilation and attention performed best.  ( 2 min )

While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion. Current methods develop independent pipelines for 2D and 3D semi-supervised learning despite the availability of paired image and point cloud frames. Observing that the distinct characteristics of each sensor cause them to be biased towards detecting different objects, we propose DetMatch, a flexible framework for joint semi-supervised learning on 2D and 3D modalities. By identifying objects detected in both sensors, our pipeline generates a cleaner, more robust set of pseudo-labels that both demonstrates stronger performance and stymies single-modality error propagation. Further, we leverage the richer semantics of RGB images to rectify incorrect 3D class predictions and improve localization of 3D boxes. Evaluating on the challenging KITTI and Waymo datasets, we improve upon strong semi-supervised learning methods and observe higher quality pseudo-labels. Code will be released at https://github.com/Divadi/DetMatch  ( 2 min )

This paper presents a new reachability analysis approach to compute interval over-approximations of the output set of feedforward neural networks with input uncertainty. We adapt to neural networks an existing mixed-monotonicity method for the reachability analysis of dynamical systems and apply it to each partial network within the main network. This ensures that the intersection of the obtained results is the tightest interval over-approximation of the output of each layer that can be obtained using mixed-monotonicity on any partial network decomposition. Unlike other tools in the literature focusing on small classes of piecewise-affine or monotone activation functions, the main strength of our approach is its generality: it can handle neural networks with any Lipschitz-continuous activation function. In addition, the simplicity of our framework allows users to very easily add unimplemented activation functions, by simply providing the function, its derivative and the global argmin and argmax of the derivative. Our algorithm is compared to five other interval-based tools (Interval Bound Propagation, ReluVal, Neurify, VeriNet, CROWN) on both existing benchmarks and two sets of small and large randomly generated networks for four activation functions (ReLU, TanH, ELU, SiLU).  ( 2 min )

The author's recent research papers, "Cumulative deviation of a subpopulation from the full population" and "A graphical method of cumulative differences between two subpopulations" (both published in volume 8 of Springer's open-access "Journal of Big Data" during 2021), propose graphical methods and summary statistics, without extensively calibrating formal significance tests. The summary metrics and methods can measure the calibration of probabilistic predictions and can assess differences in responses between a subpopulation and the full population while controlling for a covariate or score via conditioning on it. These recently published papers construct significance tests based on the scalar summary statistics, but only sketch how to calibrate the attained significance levels (also known as "P-values") for the tests. The present article reviews and synthesizes work spanning many decades in order to detail how to calibrate the P-values. The present paper presents computationally efficient, easily implemented numerical methods for evaluating properly calibrated P-values, together with rigorous mathematical proofs guaranteeing their accuracy, and illustrates and validates the methods with open-source software and numerical examples.  ( 2 min )

Deep learning methods have been successful in solving tasks in machine learning and have made breakthroughs in many sectors owing to their ability to automatically extract features from unstructured data. However, their performance relies on manual trial-and-error processes for selecting an appropriate network architecture, hyperparameters for training, and pre-/post-procedures. Even though it has been shown that network architecture plays a critical role in learning feature representation feature from data and the final performance, searching for the best network architecture is computationally intensive and heavily relies on researchers' experience. Automated machine learning (AutoML) and its advanced techniques i.e. Neural Architecture Search (NAS) have been promoted to address those limitations. Not only in general computer vision tasks, but NAS has also motivated various applications in multiple areas including medical imaging. In medical imaging, NAS has significant progress in improving the accuracy of image classification, segmentation, reconstruction, and more. However, NAS requires the availability of large annotated data, considerable computation resources, and pre-defined tasks. To address such limitations, meta-learning has been adopted in the scenarios of few-shot learning and multiple tasks. In this book chapter, we first present a brief review of NAS by discussing well-known approaches in search space, search strategy, and evaluation strategy. We then introduce various NAS approaches in medical imaging with different applications such as classification, segmentation, detection, reconstruction, etc. Meta-learning in NAS for few-shot learning and multiple tasks is then explained. Finally, we describe several open problems in NAS.  ( 2 min )

The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing high quality representations could be of great value. It is key to enabling transfer learning, which is especially beneficial for medical applications, where there is an abundance of data but labeling is costly and time consuming. We propose an unsupervised contrastive learning framework that is motivated from the perspective of label smoothing. The proposed approach uses a novel contrastive loss that naturally exploits a data augmentation scheme in which new samples are generated by mixing two data samples with a mixing component. The task in the proposed framework is to predict the mixing component, which is utilized as soft targets in the loss function. Experiments demonstrate the framework's superior performance compared to other representation learning approaches on both univariate and multivariate time series and illustrate its benefits for transfer learning for clinical time series.  ( 2 min )

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated into any type of GNN and is strictly more expressive (in terms the Weisfeiler--Lehman graph isomorphism test) than message-passing GNNs. Augmenting GNNs with TOGL leads to improved predictive performance for graph and node classification tasks, both on synthetic data sets, which can be classified by humans using their topology but not by ordinary GNNs, and on real-world data.  ( 2 min )

We study the problem of aligning the supports of distributions. Compared to the existing work on distribution alignment, support alignment does not require the densities to be matched. We propose symmetric support difference as a divergence measure to quantify the mismatch between supports. We show that select discriminators (e.g. discriminator trained for Jensen-Shannon divergence) are able to map support differences as support differences in their one-dimensional output space. Following this result, our method aligns supports by minimizing a symmetrized relaxed optimal transport cost in the discriminator 1D space via an adversarial process. Furthermore, we show that our approach can be viewed as a limit of existing notions of alignment by increasing transportation assignment tolerance. We quantitatively evaluate the method across domain adaptation tasks with shifts in label distributions. Our experiments show that the proposed method is more robust against these shifts than other alignment-based baselines.

We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space and carefully deal with the induced model misspecification. Our approach is conceptually very different from existing works that can either only handle low-dimensional linear bandits or passively deal with model misspecification. We showcase the application of our approach to two pure exploration settings that were previously under-studied: (1) the reward function belongs to a possibly infinite-dimensional Reproducing Kernel Hilbert Space, and (2) the reward function is nonlinear and can be approximated by neural networks. Our main results provide sample complexity guarantees that only depend on the effective dimension of the feature spaces in the kernel or neural representations. Extensive experiments conducted on both synthetic and real-world datasets demonstrate the efficacy of our methods.  ( 2 min )

This paper presents a speech intelligibility model based on automatic speech recognition (ASR), combining phoneme probabilities from deep neural networks (DNN) and a performance measure that estimates the word error rate from these probabilities. This model does not require the clean speech reference nor the word labels during testing as the ASR decoding step, which finds the most likely sequence of words given phoneme posterior probabilities, is omitted. The model is evaluated via the root-mean-squared error between the predicted and observed speech reception thresholds from eight normal-hearing listeners. The recognition task consists of identifying noisy words from a German matrix sentence test. The speech material was mixed with eight noise maskers covering different modulation types, from speech-shaped stationary noise to a single-talker masker. The prediction performance is compared to five established models and an ASR-model using word labels. Two combinations of features and networks were tested. Both include temporal information either at the feature level (amplitude modulation filterbanks and a feed-forward network) or captured by the architecture (mel-spectrograms and a time-delay deep neural network, TDNN). The TDNN model is on par with the DNN while reducing the number of parameters by a factor of 37; this optimization allows parallel streams on dedicated hearing aid hardware as a forward-pass can be computed within the 10ms of each frame. The proposed model performs almost as well as the label-based model and produces more accurate predictions than the baseline models.  ( 2 min )

Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative modeling. Some of their advantages are a sound theoretic foundation and an easy implementation in current deep learning frameworks. While neural fields have been applied to signals on manifolds, e.g., for texture reconstruction, their representation has been limited to extrinsically embedding the shape into Euclidean space. The extrinsic embedding ignores known intrinsic manifold properties and is inflexible wrt. transfer of the learned function. To overcome these limitations, this work introduces intrinsic neural fields, a novel and versatile representation for neural fields on manifolds. Intrinsic neural fields combine the advantages of neural fields with the spectral properties of the Laplace-Beltrami operator. We show theoretically that intrinsic neural fields inherit many desirable properties of the extrinsic neural field framework but exhibit additional intrinsic qualities, like isometry invariance. In experiments, we show intrinsic neural fields can reconstruct high-fidelity textures from images with state-of-the-art quality and are robust to the discretization of the underlying manifold. We demonstrate the versatility of intrinsic neural fields by tackling various applications: texture transfer between deformed shapes & different shapes, texture reconstruction from real-world images with view dependence, and discretization-agnostic learning on meshes and point clouds.  ( 2 min )

Standardized file formats play a key role in the development and use of computer software. However, it is possible to abuse standardized file formats by creating a file that is valid in multiple file formats. The resulting polyglot (many languages) file can confound file format identification, allowing elements of the file to evade analysis.This is especially problematic for malware detection systems that rely on file format identification for feature extraction. File format identification processes that depend on file signatures can be easily evaded thanks to flexibility in the format specifications of certain file formats. Although work has been done to identify file formats using more comprehensive methods than file signatures, accurate identification of polyglot files remains an open problem. Since malware detection systems routinely perform file format-specific feature extraction, polyglot files need to be filtered out prior to ingestion by these systems. Otherwise, malicious content could pass through undetected. To address the problem of polyglot detection we assembled a data set using the mitra tool. We then evaluated the performance of the most commonly used file identification tool, file. Finally, we demonstrated the accuracy, precision, recall and F1 score of a range of machine and deep learning models. Malconv2 and Catboost demonstrated the highest recall on our data set with 95.16% and 95.34%, respectively. These models can be incorporated into a malware detector's file processing pipeline to filter out potentially malicious polyglots before file format-dependent feature extraction takes place.  ( 2 min )

Denoising diffusion probabilistic models are a promising new class of generative models that are competitive with GANs on perceptual metrics. In this paper, we explore their potential for sequentially generating video. Inspired by recent advances in neural video compression, we use denoising diffusion models to stochastically generate a residual to a deterministic next-frame prediction. We compare this approach to two sequential VAE and two GAN baselines on four datasets, where we test the generated frames for perceptual quality and forecasting accuracy against ground truth frames. We find significant improvements in terms of perceptual quality on all data and improvements in terms of frame forecasting for complex high-resolution videos.  ( 2 min )

Increasing the semantic understanding and contextual awareness of machine learning models is important for improving robustness and reducing susceptibility to data shifts. In this work, we leverage contextual awareness for the anomaly detection problem. Although graphed-based anomaly detection has been widely studied, context-dependent anomaly detection is an open problem and without much current research. We develop a general framework for converting a context-dependent anomaly detection problem to a link prediction problem, allowing well-established techniques from this domain to be applied. We implement a system based on our framework that utilizes knowledge graph embedding models and demonstrates the ability to detect outliers using context provided by a semantic knowledge base. We show that our method can detect context-dependent anomalies with a high degree of accuracy and show that current object detectors can detect enough classes to provide the needed context for good performance within our example domain.  ( 2 min )

We prove rigorous bounds on the errors resulting from the approximation of the incompressible Navier-Stokes equations with (extended) physics informed neural networks. We show that the underlying PDE residual can be made arbitrarily small for tanh neural networks with two hidden layers. Moreover, the total error can be estimated in terms of the training error, network size and number of quadrature points. The theory is illustrated with numerical experiments.  ( 2 min )

Electroencephalogram (EEG) signals are complex, non-linear, and non-stationary in nature. However, previous studies that applied decomposition to minimize the complexity mainly exploited the hand-engineering features, limiting the information learned in EEG decoding. Therefore, extracting additional primary features from different disassembled components to improve the EEG-based recognition performance remains challenging. On the other hand, attempts have been made to use a single model to learn the hand-engineering features. Less work has been done to improve the generalization ability through ensemble learning. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. CNNs, in particular, automatically learn the primary features from raw disassembled components but not handcraft features. The first option is to fuse the obtained score before the Softmax layer and execute back-propagation on the entire ensemble network, whereas the other is to fuse the probability output of the Softmax layer. Moreover, a component-specific batch normalization (CSBN) layer is employed to reduce subject variability. Against the challenging cross-subject driver fatigue-related situation awareness (SA) recognition task, eight models are proposed under the framework, which all showed superior performance than the strong baselines. The performance of different decomposition methods and ensemble modes were further compared. Results indicated that discrete wavelet transform (DWT)-based ensemble CNN achieves the best 82.11% among the proposed models. Our framework can be simply extended to any CNN architecture and applied in any EEG-related sectors, opening the possibility of extracting more preliminary information from complex EEG data.  ( 2 min )

Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model to be served to all clients, but this approach might be sub-optimal when clients' local data distributions are heterogeneous. In order to tackle this limitation, recent personalized federated learning methods train a separate model for each client while still leveraging the knowledge available at other clients. In this work, we exploit the ability of deep neural networks to extract high quality vectorial representations (embeddings) from non-tabular data, e.g., images and text, to propose a personalization mechanism based on local memorization. Personalization is obtained interpolating a pre-trained global model with a $k$-nearest neighbors (kNN) model based on the shared representation provided by the global model. We provide generalization bounds for the proposed approach and we show on a suite of federated datasets that this approach achieves significantly higher accuracy and fairness than state-of-the-art methods.  ( 2 min )

Automated decision support systems that are able to infer second opinions from experts can potentially facilitate a more efficient allocation of resources; they can help decide when and from whom to seek a second opinion. In this paper, we look at the design of this type of support systems from the perspective of counterfactual inference. We focus on a multiclass classification setting and first show that, if experts make predictions on their own, the underlying causal mechanism generating their predictions needs to satisfy a desirable set invariant property. Further, we show that, for any causal mechanism satisfying this property, there exists an equivalent mechanism where the predictions by each expert are generated by independent sub-mechanisms governed by a common noise. This motivates the design of a set invariant Gumbel-Max structural causal model where the structure of the noise governing the sub-mechanisms underpinning the model depends on an intuitive notion of similarity between experts which can be estimated from data. Experiments on both synthetic and real data show that our model can be used to infer second opinions more accurately than its non-causal counterpart.  ( 2 min )

In many prediction problems, spurious correlations are induced by a changing relationship between the label and a nuisance variable that is also correlated with the covariates. For example, in classifying animals in natural images, the background, which is a nuisance, can predict the type of animal. This nuisance-label relationship does not always hold, and the performance of a model trained under one such relationship may be poor on data with a different nuisance-label relationship. To build predictive models that perform well regardless of the nuisance-label relationship, we develop Nuisance-Randomized Distillation (NURD). We introduce the nuisance-randomized distribution, a distribution where the nuisance and the label are independent. Under this distribution, we define the set of representations such that conditioning on any member, the nuisance and the label remain independent. We prove that the representations in this set always perform better than chance, while representations outside of this set may not. NURD finds a representation from this set that is most informative of the label under the nuisance-randomized distribution, and we prove that this representation achieves the highest performance regardless of the nuisance-label relationship. We evaluate NURD on several tasks including chest X-ray classification where, using non-lung patches as the nuisance, NURD produces models that predict pneumonia under strong spurious correlations.  ( 2 min )

While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost. The sliced Wasserstein distance and its variants improve the computational efficiency through the random projection, yet they suffer from low accuracy if the number of projections is not sufficiently large, because the majority of projections result in trivially small values. In this work, we propose a new family of distance metrics, called augmented sliced Wasserstein distances (ASWDs), constructed by first mapping samples to higher-dimensional hypersurfaces parameterized by neural networks. It is derived from a key observation that (random) linear projections of samples residing on these hypersurfaces would translate to much more flexible nonlinear projections in the original sample space, so they can capture complex structures of the data distribution. We show that the hypersurfaces can be optimized by gradient ascent efficiently. We provide the condition under which the ASWD is a valid metric and show that this can be obtained by an injective neural network architecture. Numerical results demonstrate that the ASWD significantly outperforms other Wasserstein variants for both synthetic and real-world problems.  ( 2 min )

For marketing, we sometimes need to recommend content for multiple pages in sequence. Different from general sequential decision making process, the use cases have a simpler flow where customers per seeing recommended content on each page can only return feedback as moving forward in the process or dropping from it until a termination state. We refer to this type of problems as sequential decision making in linear--flow. We propose to formulate the problem as an MDP with Bandits where Bandits are employed to model the transition probability matrix. At recommendation time, we use Thompson sampling (TS) to sample the transition probabilities and allocate the best series of actions with analytical solution through exact dynamic programming. The way that we formulate the problem allows us to leverage TS's efficiency in balancing exploration and exploitation and Bandit's convenience in modeling actions' incompatibility. In the simulation study, we observe the proposed MDP with Bandits algorithm outperforms Q-learning with $\epsilon$-greedy and decreasing $\epsilon$, independent Bandits, and interaction Bandits. We also find the proposed algorithm's performance is the most robust to changes in the across-page interdependence strength.  ( 2 min )

Learned communication between agents is a powerful tool when approaching decision-making problems that are hard to overcome by any single agent in isolation. However, continual coordination and communication learning between machine agents or human-machine partnerships remains a challenging open problem. As a stepping stone toward solving the continual communication learning problem, in this paper we contribute a multi-faceted study into what we term Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making by another agent with different perceptual access to their shared environment. We seek to establish how different temporal processes and representational choices impact Pavlovian signalling between learning agents. To do so, we introduce a partially observable decision-making domain we call the Frost Hollow. In this domain a prediction learning agent and a reinforcement learning agent are coupled into a two-part decision-making system that seeks to acquire sparse reward while avoiding time-conditional hazards. We evaluate two domain variations: 1) machine prediction and control learning in a linear walk, and 2) a prediction learning machine interacting with a human participant in a virtual reality environment. Our results showcase the speed of learning for Pavlovian signalling, the impact that different temporal representations do (and do not) have on agent-agent coordination, and how temporal aliasing impacts agent-agent and human-agent interactions differently. As a main contribution, we establish Pavlovian signalling as a natural bridge between fixed signalling paradigms and fully adaptive communication learning. Our results therefore point to an actionable, constructivist path towards continual communication learning between reinforcement learning agents, with potential impact in a range of real-world settings.  ( 3 min )

This paper considers the complex problem of a team of UAVs searching targets under uncertainty. The goal of the UAV team is to find all of the moving targets as quickly as possible before they arrive at their selected goal. The uncertainty considered is threefold: First, the UAVs do not know the targets' locations and destinations. Second, the sensing capabilities of the UAVs are not perfect. Third, the targets' movement model is unknown. We suggest a real-time algorithmic framework for the UAVs, combining entropy and stochastic-temporal belief, that aims at optimizing the probability of a quick and successful detection of all of the targets. We have empirically evaluated the algorithmic framework, and have shown its efficiency and significant performance improvement compared to other solutions. Furthermore, we have evaluated our framework using Peer Designed Agents (PDAs), which are computer agents that simulate targets and show that our algorithmic framework outperforms other solutions in this scenario.  ( 2 min )

Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. However, in general clustering tasks, the graph structure of data does not exist such that the strategy to construct a graph is crucial for performance. Therefore, how to extend graph convolution networks into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-Euclidean structure sufficiently. We further design a novel mechanism with rigorous analysis to avoid the collapse caused by the adaptive construction. Via combining the generative model for network embedding and graph-based clustering, a graph auto-encoder with a novel decoder is developed such that it performs well in weighted graph used scenarios. Extensive experiments prove the superiority of our model.  ( 2 min )

We present FourierMask, which employs Fourier series combined with implicit neural representations to generate instance segmentation masks. We apply a Fourier mapping (FM) to the coordinate locations and utilize the mapped features as inputs to an implicit representation (coordinate-based multi-layer perceptron (MLP)). FourierMask learns to predict the coefficients of the FM for a particular instance, and therefore adapts the FM to a specific object. This allows FourierMask to be generalized to predict instance segmentation masks from natural images. Since implicit functions are continuous in the domain of input coordinates, we illustrate that by sub-sampling the input pixel coordinates, we can generate higher resolution masks during inference. Furthermore, we train a renderer MLP (FourierRend) on the uncertain predictions of FourierMask and illustrate that it significantly improves the quality of the masks. FourierMask shows competitive results on the MS COCO dataset compared to the baseline Mask R-CNN at the same output resolution and surpasses it on higher resolution.  ( 2 min )

In this work, we focus on the challenging task, neuro-disease classification, using functional magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph convolutional neural networks (GCNs) have achieved remarkable success. However, these achievements are inseparable from abundant labeled data and sensitive to spurious signals. To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE. Concretely, it is demanding to design a suitable and effective SSL strategy to extract formation and robust features for fMRI. To this end, we investigate several new graph augmentation strategies from fMRI dynamic functional connectives (FC) for SSL training. Further, we leverage canonical-correlation analysis (CCA) on different temporal embeddings and present the theoretical implications. Consequently, this yields a novel two-step GCN learning procedure comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning on a small labeled fMRI dataset for a classification task. Our method is tested on two independent fMRI datasets, demonstrating superior performance on autism and dementia diagnosis.  ( 2 min )

Asynchronous and parallel implementation of standard reinforcement learning (RL) algorithms is a key enabler of the tremendous success of modern RL. Among many asynchronous RL algorithms, arguably the most popular and effective one is the asynchronous advantage actor-critic (A3C) algorithm. Although A3C is becoming the workhorse of RL, its theoretical properties are still not well-understood, including its non-asymptotic analysis and the performance gain of parallelism (a.k.a. linear speedup). This paper revisits the A3C algorithm and establishes its non-asymptotic convergence guarantees. Under both i.i.d. and Markovian sampling, we establish the local convergence guarantee for A3C in the general policy approximation case and the global convergence guarantee in softmax policy parameterization. Under i.i.d. sampling, A3C obtains sample complexity of $\mathcal{O}(\epsilon^{-2.5}/N)$ per worker to achieve $\epsilon$ accuracy, where $N$ is the number of workers. Compared to the best-known sample complexity of $\mathcal{O}(\epsilon^{-2.5})$ for two-timescale AC, A3C achieves \emph{linear speedup}, which justifies the advantage of parallelism and asynchrony in AC algorithms theoretically for the first time. Numerical tests on synthetic environment, OpenAI Gym environments and Atari games have been provided to verify our theoretical analysis.  ( 2 min )

On-demand labor platforms aim to train a skilled workforce to serve its incoming demand for jobs. Since limited jobs are available for training, and it is usually not necessary to train all workers, efficient matching of training jobs requires prioritizing fast learners over slow ones. However, the learning rates of novice workers are unknown, resulting in a tradeoff between exploration (learning the learning rates) and exploitation (training the best workers). Motivated to study this tradeoff, we analyze a novel objective within the stochastic multi-armed bandit framework. Given $K$ arms, instead of maximizing the expected total reward from $T$ pulls (the traditional "sum" objective), we consider the vector of cumulative rewards earned from the $K$ arms at the end of $T$ pulls and aim to maximize the expected highest cumulative reward (the "max" objective). When rewards represent skill increments, this corresponds to the objective of training a single highly skilled worker from a set of novice workers, using a limited supply of training jobs. For this objective, we show that any policy must incur an instance-dependent asymptotic regret of $\Omega(\log T)$ (with a higher instance-dependent constant) and a worst-case regret of $\Omega(K^{1/3}T^{2/3})$. We then design an explore-then-commit policy featuring exploration based on appropriately tuned confidence bounds on the mean reward and an adaptive stopping criterion, which adapts to the problem difficulty and achieves these bounds (up to logarithmic factors). We generalize our algorithmic insights to the problem of maximizing the expected value of the average cumulative reward of the top $m$ arms with the highest cumulative rewards, corresponding to the case where multiple workers must be trained. Our numerical experiments demonstrate the efficacy of our policies compared to several natural alternatives in practical parameter regimes.  ( 3 min )

Image super-resolution (SR) techniques are used to generate a high-resolution image from a low-resolution image. Until now, deep generative models such as autoregressive models and Generative Adversarial Networks (GANs) have proven to be effective at modelling high-resolution images. Models based on Variational Autoencoders (VAEs) have often been criticized for their feeble generative performance, but with new advancements such as VDVAE (very deep VAE), there is now strong evidence that deep VAEs have the potential to outperform current state-of-the-art models for high-resolution image generation. In this paper, we introduce VDVAE-SR, a new model that aims to exploit the most recent deep VAE methodologies to improve upon image super-resolution using transfer learning on pretrained VDVAEs. Through qualitative and quantitative evaluations, we show that the proposed model is competitive with other state-of-the-art methods.  ( 2 min )

The Lov\'asz hinge is a convex surrogate recently proposed for structured binary classification, in which $k$ binary predictions are made simultaneously and the error is judged by a submodular set function. Despite its wide usage in image segmentation and related problems, its consistency has remained open. We resolve this open question, showing that the Lov\'asz hinge is inconsistent for its desired target unless the set function is modular. Leveraging a recent embedding framework, we instead derive the target loss for which the Lov\'asz hinge is consistent. This target, which we call the structured abstain problem, allows one to abstain on any subset of the $k$ predictions. We derive two link functions, each of which are consistent for all submodular set functions simultaneously.  ( 2 min )

Split computing distributes the execution of a neural network (e.g., for a classification task) between a mobile device and a more powerful edge server. A simple alternative to splitting the network is to carry out the supervised task purely on the edge server while compressing and transmitting the full data, and most approaches have barely outperformed this baseline. This paper proposes a new approach for discretizing and entropy-coding intermediate feature activations to efficiently transmit them from the mobile device to the edge server. We show that a efficient splittable network architecture results from a three-way tradeoff between (a) minimizing the computation on the mobile device, (b) minimizing the size of the data to be transmitted, and (c) maximizing the model's prediction performance. We propose an architecture based on this tradeoff and train the splittable network and entropy model in a knowledge distillation framework. In an extensive set of experiments involving three vision tasks, three datasets, nine baselines, and more than 180 trained models, we show that our approach improves supervised rate-distortion tradeoffs while maintaining a considerably smaller encoder size. We also release sc2bench, an installable Python package, to encourage and facilitate future studies on supervised compression for split computing (SC2).  ( 2 min )

In a high-dimensional regression framework, we study consequences of the naive two-step procedure where first the dimension of the input variables is reduced and second, the reduced input variables are used to predict the output variable. More specifically we combine principal component analysis (PCA) with kernel regression. In order to analyze the resulting regression errors, a novel stability result of kernel regression with respect to the Wasserstein distance is derived. This allows us to bound errors that occur when perturbed input data is used to fit a kernel function. We combine the stability result with known estimates from the literature on both principal component analysis and kernel regression to obtain convergence rates for the two-step procedure.  ( 2 min )

Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries.  ( 2 min )

This paper proposes an algorithm (RMDA) for training neural networks (NNs) with a regularization term for promoting desired structures. RMDA does not incur computation additional to proximal SGD with momentum, and achieves variance reduction without requiring the objective function to be of the finite-sum form. Through the tool of manifold identification from nonlinear optimization, we prove that after a finite number of iterations, all iterates of RMDA possess a desired structure identical to that induced by the regularizer at the stationary point of asymptotic convergence, even in the presence of engineering tricks like data augmentation and dropout that complicate the training process. Experiments on training NNs with structured sparsity confirm that variance reduction is necessary for such an identification, and show that RMDA thus significantly outperforms existing methods for this task. For unstructured sparsity, RMDA also outperforms a state-of-the-art pruning method, validating the benefits of training structured NNs through regularization.  ( 2 min )

We present a general-purpose framework for image modelling and vision tasks based on probabilistic frame prediction. Our approach unifies a broad range of tasks, from image segmentation, to novel view synthesis and video interpolation. We pair this framework with an architecture we term Transframer, which uses U-Net and Transformer components to condition on annotated context frames, and outputs sequences of sparse, compressed image features. Transframer is the state-of-the-art on a variety of video generation benchmarks, is competitive with the strongest models on few-shot view synthesis, and can generate coherent 30 second videos from a single image without any explicit geometric information. A single generalist Transframer simultaneously produces promising results on 8 tasks, including semantic segmentation, image classification and optical flow prediction with no task-specific architectural components, demonstrating that multi-task computer vision can be tackled using probabilistic image models. Our approach can in principle be applied to a wide range of applications that require learning the conditional structure of annotated image-formatted data.  ( 2 min )

Given a large dataset for training, GANs can achieve remarkable performance for the image synthesis task. However, training GANs in extremely low data regimes remains a challenge, as overfitting often occurs, leading to memorization or training divergence. In this work, we introduce SIV-GAN, an unconditional generative model that can generate new scene compositions from a single training image or a single video clip. We propose a two-branch discriminator architecture, with content and layout branches designed to judge internal content and scene layout realism separately from each other. This discriminator design enables synthesis of visually plausible, novel compositions of a scene, with varying content and layout, while preserving the context of the original sample. Compared to previous single-image GANs, our model generates more diverse, higher quality images, while not being restricted to a single image setting. We show that SIV-GAN successfully deals with a new challenging task of learning from a single video, for which prior GAN models fail to achieve synthesis of both high quality and diversity.  ( 2 min )

In parallel with the rapid adoption of Artificial Intelligence (AI) empowered by advances in AI research, there have been growing awareness and concerns of data privacy. Recent significant developments in the data regulation landscape have prompted a seismic shift in interest towards privacy-preserving AI. This has contributed to the popularity of Federated Learning (FL), the leading paradigm for the training of machine learning models on data silos in a privacy-preserving manner. In this survey, we explore the domain of Personalized FL (PFL) to address the fundamental challenges of FL on heterogeneous data, a universal characteristic inherent in all real-world datasets. We analyze the key motivations for PFL and present a unique taxonomy of PFL techniques categorized according to the key challenges and personalization strategies in PFL. We highlight their key ideas, challenges and opportunities and envision promising future trajectories of research towards new PFL architectural design, realistic PFL benchmarking, and trustworthy PFL approaches.  ( 2 min )

Graph neural networks (GNNs) have been extensively developed for graph representation learning in various application domains. However, similar to all other neural networks models, GNNs suffer from the black-box problem as people cannot understand the mechanism underlying them. To solve this problem, several GNN explainability methods have been proposed to explain the decisions made by GNNs. In this survey, we give an overview of the state-of-the-art GNN explainability methods and how they are evaluated. Furthermore, we propose a new evaluation metric and conduct thorough experiments to compare GNN explainability methods on real world datasets. We also suggest future directions for GNN explainability.  ( 2 min )

We currently witness the spectacular success of artificial intelligence in both science and public life. However, the development of a rigorous mathematical foundation is still at an early stage. In this survey article, which is based on an invited lecture at the International Congress of Mathematicians 2022, we will in particular focus on the current "workhorse" of artificial intelligence, namely deep neural networks. We will present the main theoretical directions along with several exemplary results and discuss key open problems.  ( 2 min )

Every uncalibrated classifier has a corresponding true calibration map that calibrates its confidence. Deviations of this idealistic map from the identity map reveal miscalibration. Such calibration errors can be reduced with many post-hoc calibration methods which fit some family of calibration maps on a validation dataset. In contrast, evaluation of calibration with the expected calibration error (ECE) on the test set does not explicitly involve fitting. However, as we demonstrate, ECE can still be viewed as if fitting a family of functions on the test data. This motivates the fit-on-the-test view on evaluation: first, approximate a calibration map on the test data, and second, quantify its distance from the identity. Exploiting this view allows us to unlock missed opportunities: (1) use the plethora of post-hoc calibration methods for evaluating calibration; (2) tune the number of bins in ECE with cross-validation. Furthermore, we introduce: (3) benchmarking on pseudo-real data where the true calibration map can be estimated very precisely; and (4) novel calibration and evaluation methods using new calibration map families PL and PL3.  ( 2 min )

The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U$^2$-Net. Two statistical methods for deep neural networks are utilized: the bootstrap and the Monte Carlo (MC) dropout. The detailed analysis of the uncertainties for the DM predictions leads to a deeper understanding of the DM model's deficiencies. Based on our investigation, we propose a self-normalization module in the network. The improved network model, called Self-Normalized Density Map (SNDM), can correct its output density map by itself to accurately predict the total number of objects in the image. The SNDM architecture outperforms the original model. Moreover, both statistical frameworks -- bootstrap and MC dropout -- have consistent statistical results for SNDM, which were not observed in the original model.  ( 2 min )

Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunately, it was shown that these architectures are limited in their expressive power. This paper proposes a novel framework called Equivariant Subgraph Aggregation Networks (ESAN) to address this issue. Our main observation is that while two graphs may not be distinguishable by an MPNN, they often contain distinguishable subgraphs. Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture. We develop novel variants of the 1-dimensional Weisfeiler-Leman (1-WL) test for graph isomorphism, and prove lower bounds on the expressiveness of ESAN in terms of these new WL variants. We further prove that our approach increases the expressive power of both MPNNs and more expressive architectures. Moreover, we provide theoretical results that describe how design choices such as the subgraph selection policy and equivariant neural architecture affect our architecture's expressive power. To deal with the increased computational cost, we propose a subgraph sampling scheme, which can be viewed as a stochastic version of our framework. A comprehensive set of experiments on real and synthetic datasets demonstrates that our framework improves the expressive power and overall performance of popular GNN architectures.  ( 2 min )

Large-scale monitoring, anomaly detection, and root cause analysis of metrics are essential requirements of the internet-services industry. To address the need to continuously monitor millions of metrics, many anomaly detection approaches are being used on a daily basis by large internet-based companies. However, in spite of the significant progress made to accurately and efficiently detect anomalies in metrics, the sheer scale of the number of metrics has meant there are still a large number of false alarms that need to be investigated. This paper presents a framework for reliable large-scale anomaly detection. It is significantly more accurate than existing approaches and allows for easy interpretation of models, thus enabling practical data monitoring in the internet-services domain.  ( 2 min )

Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge. However, the bottleneck of current meta-learning algorithms is the requirement of a large number of meta-training tasks, which may not be accessible in real-world scenarios. To address the challenge that available tasks may not densely sample the space of tasks, we propose to augment the task set through interpolation. By meta-learning with task interpolation (MLTI), our approach effectively generates additional tasks by randomly sampling a pair of tasks and interpolating the corresponding features and labels. Under both gradient-based and metric-based meta-learning settings, our theoretical analysis shows MLTI corresponds to a data-adaptive meta-regularization and further improves the generalization. Empirically, in our experiments on eight datasets from diverse domains including image recognition, pose prediction, molecule property prediction, and medical image classification, we find that the proposed general MLTI framework is compatible with representative meta-learning algorithms and consistently outperforms other state-of-the-art strategies.  ( 2 min )

Improving the training and inference performance of graph neural networks (GNNs) is faced with a challenge uncommon in general neural networks: creating mini-batches requires a lot of computation and data movement due to the exponential growth of multi-hop graph neighborhoods along network layers. Such a unique challenge gives rise to a diverse set of system design choices. We argue in favor of performing mini-batch training with neighborhood sampling in a distributed multi-GPU environment, under which we identify major performance bottlenecks hitherto under-explored by developers: mini-batch preparation and transfer. We present a sequence of improvements to mitigate these bottlenecks, including a performance-engineered neighborhood sampler, a shared-memory parallelization strategy, and the pipelining of batch transfer with GPU computation. We also conduct an empirical analysis that supports the use of sampling for inference, showing that test accuracies are not materially compromised. Such an observation unifies training and inference, simplifying model implementation. We report comprehensive experimental results with several benchmark data sets and GNN architectures, including a demonstration that, for the ogbn-papers100M data set, our system SALIENT achieves a speedup of 3x over a standard PyTorch-Geometric implementation with a single GPU and a further 8x parallel speedup with 16 GPUs. Therein, training a 3-layer GraphSAGE model with sampling fanout (15, 10, 5) takes 2.0 seconds per epoch and inference with fanout (20, 20, 20) takes 2.4 seconds, attaining test accuracy 64.58%.  ( 2 min )

We study the properties of various over-parametrized convolutional neural architectures through their respective Gaussian process and neural tangent kernels. We prove that, with normalized multi-channel input and ReLU activation, the eigenfunctions of these kernels with the uniform measure are formed by products of spherical harmonics, defined over the channels of the different pixels. We next use hierarchical factorizable kernels to bound their respective eigenvalues. We show that the eigenvalues decay polynomially, quantify the rate of decay, and derive measures that reflect the composition of hierarchical features in these networks. Our results provide concrete quantitative characterization of over-parameterized convolutional network architectures.  ( 2 min )

In this paper, we introduce a novel neural network training framework that increases model's adversarial robustness to adversarial attacks while maintaining high clean accuracy by combining contrastive learning (CL) with adversarial training (AT). We propose to improve model robustness to adversarial attacks by learning feature representations that are consistent under both data augmentations and adversarial perturbations. We leverage contrastive learning to improve adversarial robustness by considering an adversarial example as another positive example, and aim to maximize the similarity between random augmentations of data samples and their adversarial example, while constantly updating the classification head in order to avoid a cognitive dissociation between the classification head and the embedding space. This dissociation is caused by the fact that CL updates the network up to the embedding space, while freezing the classification head which is used to generate new positive adversarial examples. We validate our method, Contrastive Learning with Adversarial Features(CLAF), on the CIFAR-10 dataset on which it outperforms both robust accuracy and clean accuracy over alternative supervised and self-supervised adversarial learning methods.  ( 2 min )

With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming especially when dealing with multi-classification tasks. In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture. Firstly, we attach each kernel with an adaptive coefficient to enhance the signal processing of the inputs. This can enhance the propagation of informative features and suppress the propagation of useless features in the early stage of the network. Then, we make full use of GA's heuristic search and the advantage of no need for the gradient to search for the best architecture of CNN. This can achieve a CNN with better performance than a handcrafted one in a large search space at the minimum cost. We verify the convergence of our data processing trick and compare the performance of traditional CNNs before and after using our trick. Meanwhile, we compare the performance between the CNN generated through NAS-G and the traditional CNNs with our trick. The experiments demonstrate that the convergence of CNNs with data processing trick is faster than without data processing trick and the CNN with data processing trick generated by NAS-G outperforms the handcrafted counterparts that use the data processing trick too.  ( 3 min )

Biological intelligence is remarkable in its ability to produce complex behaviour in many diverse situations through data efficient, generalisable and transferable skill acquisition. It is believed that learning "good" sensory representations is important for enabling this, however there is little agreement as to what a good representation should look like. In this review article we are going to argue that symmetry transformations are a fundamental principle that can guide our search for what makes a good representation. The idea that there exist transformations (symmetries) that affect some aspects of the system but not others, and their relationship to conserved quantities has become central in modern physics, resulting in a more unified theoretical framework and even ability to predict the existence of new particles. Recently, symmetries have started to gain prominence in machine learning too, resulting in more data efficient and generalisable algorithms that can mimic some of the complex behaviours produced by biological intelligence. Finally, first demonstrations of the importance of symmetry transformations for representation learning in the brain are starting to arise in neuroscience. Taken together, the overwhelming positive effect that symmetries bring to these disciplines suggest that they may be an important general framework that determines the structure of the universe, constrains the nature of natural tasks and consequently shapes both biological and artificial intelligence.  ( 2 min )

Searching for the architecture cells is a dominant paradigm in NAS. However, little attention has been devoted to the analysis of the cell-based search spaces even though it is highly important for the continual development of NAS. In this work, we conduct an empirical post-hoc analysis of architectures from the popular cell-based search spaces and find that the existing search spaces contain a high degree of redundancy: the architecture performance is minimally sensitive to changes at large parts of the cells, and universally adopted designs, like the explicit search for a reduction cell, significantly increase the complexities but have very limited impact on the performance. Across architectures found by a diverse set of search strategies, we consistently find that the parts of the cells that do matter for architecture performance often follow similar and simple patterns. By explicitly constraining cells to include these patterns, randomly sampled architectures can match or even outperform the state of the art. These findings cast doubts into our ability to discover truly novel architectures in the existing cell-based search spaces, and inspire our suggestions for improvement to guide future NAS research. Code is available at https://github.com/xingchenwan/cell-based-NAS-analysis.  ( 2 min )

Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the task-id is provided for each test sample during testing for TIL, but not provided for CIL. Continual learning methods intended for one problem have limitations on the other problem. This paper proposes a novel unified approach based on out-of-distribution (OOD) detection and task masking, called CLOM, to solve both problems. The key novelty is that each task is trained as an OOD detection model rather than a traditional supervised learning model, and a task mask is trained to protect each task to prevent forgetting. Our evaluation shows that CLOM outperforms existing state-of-the-art baselines by large margins. The average TIL/CIL accuracy of CLOM over six experiments is 87.6/67.9% while that of the best baselines is only 82.4/55.0%.  ( 2 min )

Model-agnostic meta-learning (MAML) and its variants have become popular approaches for few-shot learning. However, due to the non-convexity of deep neural nets (DNNs) and the bi-level formulation of MAML, the theoretical properties of MAML with DNNs remain largely unknown. In this paper, we first prove that MAML with over-parameterized DNNs is guaranteed to converge to global optima at a linear rate. Our convergence analysis indicates that MAML with over-parameterized DNNs is equivalent to kernel regression with a novel class of kernels, which we name as Meta Neural Tangent Kernels (MetaNTK). Then, we propose MetaNTK-NAS, a new training-free neural architecture search (NAS) method for few-shot learning that uses MetaNTK to rank and select architectures. Empirically, we compare our MetaNTK-NAS with previous NAS methods on two popular few-shot learning benchmarks, miniImageNet, and tieredImageNet. We show that the performance of MetaNTK-NAS is comparable or better than the state-of-the-art NAS method designed for few-shot learning while enjoying more than 100x speedup. We believe the efficiency of MetaNTK-NAS makes itself more practical for many real-world tasks.  ( 2 min )

In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by using medical and commercial devices. Deep neural networks (DNNs) can be used to analyze these signals because of their high accuracy rate. However, researchers have found that adversarial attacks can significantly reduce the accuracy of DNNs. Studies have been conducted to defend ECG-based DNNs against traditional adversarial attacks, such as projected gradient descent (PGD), and smooth adversarial perturbation (SAP) which targets ECG classification; however, to the best of our knowledge, no study has completely explored the defense against adversarial attacks targeting ECG classification. Thus, we did different experiments to explore the effects of defense methods against white-box adversarial attack and black-box adversarial attack targeting ECG classification, and we found that some common defense methods performed well against these attacks. Besides, we proposed a new defense method called Adversarial Distillation Training (ADT) which comes from defensive distillation and can effectively improve the generalization performance of DNNs. The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization. Furthermore, we found that our method performed better against PGD attacks with low noise levels, which means that our method has stronger robustness.  ( 2 min )

The support vector data description (SVDD) approach serves as a de facto standard for one-class classification where the learning task entails inferring the smallest hyper-sphere to enclose target objects while linearly penalising any errors/slacks via an $\ell_1$-norm penalty term. In this study, we generalise this modelling formalism to a general $\ell_p$-norm ($p\geq1$) slack penalty function. By virtue of an $\ell_p$ slack norm, the proposed approach enables formulating a non-linear cost function with respect to slacks. From a dual problem perspective, the proposed method introduces a sparsity-inducing dual norm into the objective function, and thus, possesses a higher capacity to tune into the inherent sparsity of the problem for enhanced descriptive capability. A theoretical analysis based on Rademacher complexities characterises the generalisation performance of the proposed approach in terms of parameter $p$ while the experimental results on several datasets confirm the merits of the proposed method compared to other alternatives.  ( 2 min )

A common problem in data science is "use this function defined over this small set to generate predictions over that larger set." Extrapolation, interpolation, statistical inference and forecasting all reduce to this problem. The Kan extension is a powerful tool in category theory that generalizes this notion. In this work we explore several applications of Kan extensions to data science. We begin by deriving a simple classification algorithm as a Kan extension and experimenting with this algorithm on real data. Next, we use the Kan extension to derive a procedure for learning clustering algorithms from labels and explore the performance of this procedure on real data. We then investigate how Kan extensions can be used to learn a general mapping from datasets of labeled examples to functions and to approximate a complex function with a simpler one.  ( 2 min )

This paper introduces a deep learning modular network for side-channel analysis. Our deep learning approach features the capability to exchange part of it (modules) with others networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures for each evaluation, reducing the body of work when conducting those. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation suggesting that learning transferability is possible with the network we propose in this paper.  ( 2 min )

We develop theoretically guaranteed stochastic methods for outlier-robust PCA. Outlier-robust PCA seeks an underlying low-dimensional linear subspace from a dataset that is corrupted with outliers. We are able to show that our methods, which involve stochastic geodesic gradient descent over the Grassmannian manifold, converge and recover an underlying subspace in various regimes through the development of a novel convergence analysis. The main application of this method is an effective differentially private algorithm for outlier-robust PCA that uses a Gaussian noise mechanism within the stochastic gradient method. Our results emphasize the advantages of the nonconvex methods over another convex approach to solving this problem in the differentially private setting. Experiments on synthetic and stylized data verify these results.  ( 2 min )

Low-precision arithmetic trains deep learning models using less energy, less memory and less time. However, we pay a price for the savings: lower precision may yield larger round-off error and hence larger prediction error. As applications proliferate, users must choose which precision to use to train a new model, and chip manufacturers must decide which precisions to manufacture. We view these precision choices as a hyperparameter tuning problem, and borrow ideas from meta-learning to learn the tradeoff between memory and error. In this paper, we introduce Pareto Estimation to Pick the Perfect Precision (PEPPP). We use matrix factorization to find non-dominated configurations (the Pareto frontier) with a limited number of network evaluations. For any given memory budget, the precision that minimizes error is a point on this frontier. Practitioners can use the frontier to trade memory for error and choose the best precision for their goals.  ( 2 min )

The graph neural network (GNN) has been a powerful deep-learning tool in chemistry domain, due to its close connection with molecular graphs. Most GNN models collect and update atom and molecule features from the fed atom (and, in some cases, bond) features, which are basically based on the two-dimensional (2D) graph representation of 3D molecules. Correspondingly, the adjacency matrix, containing the information on covalent bonds, or equivalent data structures (e.g., lists) have been the main core in the feature-updating processes, such as graph convolution. However, the 2D-based models do not faithfully represent 3D molecules and their physicochemical properties, exemplified by the overlooked field effect that is a "through-space" effect, not a "through-bond" effect. The GNN model proposed herein, denoted as MolNet, is chemically intuitive, accommodating the 3D non-bond information in a molecule, with a noncovalent adjacency matrix $\bf{\bar A}$, and also bond-strength information from a weighted bond matrix $\bf{B}$. The noncovalent atoms, not directly bonded to a given atom in a molecule, are identified within 5 $\unicode{x212B}$ of cut-off range for the construction of $\bf{\bar A}$, and $\bf{B}$ has edge weights of 1, 1.5, 2, and 3 for single, aromatic, double, and triple bonds, respectively. Comparative studies show that MolNet outperforms various baseline GNN models and gives a state-of-the-art performance in the classification task of BACE dataset and regression task of ESOL dataset. This work suggests a future direction of deep-learning chemistry in the construction of deep-learning models that are chemically intuitive and comparable with the existing chemistry concepts and tools.  ( 2 min )

Faithful visualizations of data residing on manifolds must take the underlying geometry into account when producing a flat planar view of the data. In this paper, we extend the classic stochastic neighbor embedding (SNE) algorithm to data on general Riemannian manifolds. We replace standard Gaussian assumptions with Riemannian diffusion counterparts and propose an efficient approximation that only requires access to calculations of Riemannian distances and volumes. We demonstrate that the approach also allows for mapping data from one manifold to another, e.g. from a high-dimensional sphere to a low-dimensional one.  ( 2 min )

Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights. We instead envision language models that can simply read and memorize new data at inference time, thus acquiring new knowledge immediately. In this work, we extend language models with the ability to memorize the internal representations of past inputs. We demonstrate that an approximate kNN lookup into a non-differentiable memory of recent (key, value) pairs improves language modeling across various benchmarks and tasks, including generic webtext (C4), math papers (arXiv), books (PG-19), code (Github), as well as formal theorems (Isabelle). We show that the performance steadily improves when we increase the size of memory up to 262K tokens. On benchmarks including code and mathematics, we find that the model is capable of making use of newly defined functions and theorems during test time.  ( 2 min )

Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks. Existing L2O models parameterize optimization rules by neural networks, and learn those numerical rules via meta-training. However, they face two common pitfalls: (1) scalability: the numerical rules represented by neural networks create extra memory overhead for applying L2O models, and limit their applicability to optimizing larger tasks; (2) interpretability: it is unclear what an L2O model has learned in its black-box optimization rule, nor is it straightforward to compare different L2O models in an explainable way. To avoid both pitfalls, this paper proves the concept that we can "kill two birds by one stone", by introducing the powerful tool of symbolic regression to L2O. In this paper, we establish a holistic symbolic representation and analysis framework for L2O, which yields a series of insights for learnable optimizers. Leveraging our findings, we further propose a lightweight L2O model that can be meta-trained on large-scale problems and outperformed human-designed and tuned optimizers. Our work is set to supply a brand-new perspective to L2O research. Codes are available at: https://github.com/VITA-Group/Symbolic-Learning-To-Optimize.  ( 2 min )

Light-induced chemical processes are ubiquitous in nature and have widespread technological applications. For example, photoisomerization can allow a drug with a photo-switchable scaffold such as azobenzene to be activated with light. In principle, photoswitches with desired photophysical properties like high isomerization quantum yields can be identified through virtual screening with reactive simulations. In practice, these simulations are rarely used for screening, since they require hundreds of trajectories and expensive quantum chemical methods to account for non-adiabatic excited state effects. Here we introduce a diabatic artificial neural network (DANN) based on diabatic states to accelerate such simulations for azobenzene derivatives. The network is six orders of magnitude faster than the quantum chemistry method used for training. DANN is transferable to azobenzene molecules outside the training set, predicting quantum yields for unseen species that are correlated with experiment. We use the model to virtually screen 3,100 hypothetical molecules, and identify novel species with extremely high predicted quantum yields. The model predictions are confirmed using high accuracy non-adiabatic dynamics. Our results pave the way for fast and accurate virtual screening of photoactive compounds.  ( 2 min )

We present a novel bi-modal system based on deep networks to address the problem of learning associations and simple meanings of objects depicted in "authored" images, such as fine art paintings and drawings. Our overall system processes both the images and associated texts in order to learn associations between images of individual objects, their identities and the abstract meanings they signify. Unlike past deep nets that describe depicted objects and infer predicates, our system identifies meaning-bearing objects ("signifiers") and their associations ("signifieds") as well as basic overall meanings for target artworks. Our system had precision of 48% and recall of 78% with an F1 metric of 0.6 on a curated set of Dutch vanitas paintings, a genre celebrated for its concentration on conveying a meaning of great import at the time of their execution. We developed and tested our system on fine art paintings but our general methods can be applied to other authored images.  ( 2 min )

We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances of vision transformers (ViTs), in this paper, we demonstrate that using a few large convolutional kernels instead of a stack of small kernels could be a more powerful paradigm. We suggested five guidelines, e.g., applying re-parameterized large depth-wise convolutions, to design efficient high-performance large-kernel CNNs. Following the guidelines, we propose RepLKNet, a pure CNN architecture whose kernel size is as large as 31x31, in contrast to commonly used 3x3. RepLKNet greatly closes the performance gap between CNNs and ViTs, e.g., achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency. RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes. Our study further reveals that, in contrast to small-kernel CNNs, large-kernel CNNs have much larger effective receptive fields, and higher shape bias rather than texture bias. Code & models at https://github.com/megvii-research/RepLKNet.  ( 2 min )

This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers have recently been thought of as a promising alternative to convolutional neural networks for visual recognition. But when there is no sufficient data, it gets stuck in overfitting and shows inferior performance. To improve data efficiency, we propose hierarchically cascaded transformers that exploit intrinsic image structures through spectral tokens pooling and optimize the learnable parameters through latent attribute surrogates. The intrinsic image structure is utilized to reduce the ambiguity between foreground content and background noise by spectral tokens pooling. And the attribute surrogate learning scheme is designed to benefit from the rich visual information in image-label pairs instead of simple visual concepts assigned by their labels. Our Hierarchically Cascaded Transformers, called HCTransformers, is built upon a self-supervised learning framework DINO and is tested on several popular few-shot learning benchmarks. In the inductive setting, HCTransformers surpass the DINO baseline by a large margin of 9.7% 5-way 1-shot accuracy and 9.17% 5-way 5-shot accuracy on miniImageNet, which demonstrates HCTransformers are efficient to extract discriminative features. Also, HCTransformers show clear advantages over SOTA few-shot classification methods in both 5-way 1-shot and 5-way 5-shot settings on four popular benchmark datasets, including miniImageNet, tieredImageNet, FC100, and CIFAR-FS. The trained weights and codes are available at https://github.com/StomachCold/HCTransformers.  ( 2 min )

There are many recent research efforts to fine-tune a pre-trained generator with a few target images to generate images of a novel domain. Unfortunately, these methods often suffer from overfitting or under-fitting when fine-tuned with a single target image. To address this, here we present a novel single-shot GAN adaptation method through unified CLIP space manipulations. Specifically, our model employs a two-step training strategy: reference image search in the source generator using a CLIP-guided latent optimization, followed by generator fine-tuning with a novel loss function that imposes CLIP space consistency between the source and adapted generators. To further improve the adapted model to produce spatially consistent samples with respect to the source generator, we also propose contrastive regularization for patchwise relationships in the CLIP space. Experimental results show that our model generates diverse outputs with the target texture and outperforms the baseline models both qualitatively and quantitatively. Furthermore, we show that our CLIP space manipulation strategy allows more effective attribute editing.  ( 2 min )

Gaussian process regression underpins countless academic and industrial applications of machine learning and statistics, with maximum likelihood estimation routinely used to select appropriate parameters for the covariance kernel. However, it remains an open problem to establish the circumstances in which maximum likelihood estimation is well-posed. That is, when the predictions of the regression model are continuous (or insensitive to small perturbations) in the training data. This article presents a rigorous proof that the maximum likelihood estimator fails to be well-posed in Hellinger distance in a scenario where the data are noiseless. The failure case occurs for any Gaussian process with a stationary covariance function whose lengthscale parameter is estimated using maximum likelihood. Although the failure of maximum likelihood estimation is informally well-known, these theoretical results appear to be the first of their kind, and suggest that well-posedness may need to be assessed post-hoc, on a case-by-case basis, when maximum likelihood estimation is used to train a Gaussian process model.  ( 2 min )

A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task. A line of research has made some progress towards this goal by studying the concentration of measure, but we argue standard concentration fails to fully characterize the intrinsic robustness of a classification problem since it ignores data labels which are essential to any classification task. Building on a novel definition of label uncertainty, we empirically demonstrate that error regions induced by state-of-the-art models tend to have much higher label uncertainty than randomly-selected subsets. This observation motivates us to adapt a concentration estimation algorithm to account for label uncertainty, resulting in more accurate intrinsic robustness measures for benchmark image classification problems.  ( 2 min )

Inspired by the numerical solution of ordinary differential equations, in this paper we propose a novel Reservoir Computing (RC) model, called the Euler State Network (EuSN). The introduced approach makes use of forward Euler discretization and antisymmetric recurrent matrices to design reservoir dynamics that are both stable and non-dissipative by construction. Our mathematical analysis shows that the resulting model is biased towards unitary effective spectral radius and zero local Lyapunov exponents, intrinsically operating at the edge of stability. Experiments on synthetic tasks indicate the marked superiority of the proposed approach, compared to standard RC models, in tasks requiring long-term memorization skills. Furthermore, results on real-world time series classification benchmarks point out that EuSN is capable of matching (or even surpassing) the level of accuracy of trainable Recurrent Neural Networks, while allowing up to 100-fold savings in computation time and energy consumption.  ( 2 min )

In this paper, we analyze the generalization performance of the Iterative Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The parameter estimation and sparsity recovery consistency of IHT has long been known in compressed sensing. From the perspective of statistical learning, another fundamental question is how well the IHT estimation would predict on unseen data. This paper makes progress towards answering this open question by introducing a novel sparse generalization theory for IHT under the notion of algorithmic stability. Our theory reveals that: 1) under natural conditions on the empirical risk function over $n$ samples of dimension $p$, IHT with sparsity level $k$ enjoys an $\mathcal{\tilde O}(n^{-1/2}\sqrt{k\log(n)\log(p)})$ rate of convergence in sparse excess risk; 2) a tighter $\mathcal{\tilde O}(n^{-1/2}\sqrt{\log(n)})$ bound can be established by imposing an additional iteration stability condition on a hypothetical IHT procedure invoked to the population risk; and 3) a fast rate of order $\mathcal{\tilde O}\left(n^{-1}k(\log^3(n)+\log(p))\right)$ can be derived for strongly convex risk function under proper strong-signal conditions. The results have been substantialized to sparse linear regression and sparse logistic regression models to demonstrate the applicability of our theory. Preliminary numerical evidence is provided to confirm our theoretical predictions.  ( 2 min )

Balancing model complexity against the information contained in observed data is the central challenge to learning. In order for complexity-efficient models to exist and be discoverable in high dimensions, we require a computational framework that relates a credible notion of complexity to simple parameter representations. Further, this framework must allow excess complexity to be gradually removed via gradient-based optimization. Our n-ary, or n-argument, activation functions fill this gap by approximating belief functions (probabilistic Boolean logic) using logit representations of probability. Just as Boolean logic determines the truth of a consequent claim from relationships among a set of antecedent propositions, probabilistic formulations generalize predictions when antecedents, truth tables, and consequents all retain uncertainty. Our activation functions demonstrate the ability to learn arbitrary logic, such as the binary exclusive disjunction (p xor q) and ternary conditioned disjunction ( c ? p : q ), in a single layer using an activation function of matching or greater arity. Further, we represent belief tables using a basis that directly associates the number of nonzero parameters to the effective arity of the belief function, thus capturing a concrete relationship between logical complexity and efficient parameter representations. This opens optimization approaches to reduce logical complexity by inducing parameter sparsity.  ( 2 min )

Self-supervised learning, especially contrastive learning, has made an outstanding contribution to the development of many deep learning research fields. Recently, researchers in the acoustic signal processing field noticed its success and leveraged contrastive learning for better music representation. Typically, existing approaches maximize the similarity between two distorted audio segments sampled from the same music. In other words, they ensure a semantic agreement at the music level. However, those coarse-grained methods neglect some inessential or noisy elements at the frame level, which may be detrimental to the model to learn the effective representation of music. Towards this end, this paper proposes a novel Positive-nEgative frame mask for Music Representation based on the contrastive learning framework, abbreviated as PEMR. Concretely, PEMR incorporates a Positive-Negative Mask Generation module, which leverages transformer blocks to generate frame masks on the Log-Mel spectrogram. We can generate self-augmented negative and positive samples by masking important components or inessential components, respectively. We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music. We conduct experiments on four public datasets. The experimental results of two music-related downstream tasks, music classification, and cover song identification, demonstrate the generalization ability and transferability of music representation learned by PEMR.  ( 2 min )

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning \mbox{methods} with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions. However, more general situations, such as instance-dependent label noise, have not been fully explored, as scarce studies focus on their label corruption process. By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. The resultant model can be realized by DNNs, where the training procedure is accomplished by employing an alternating optimization algorithm. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness over state-of-the-art counterparts.  ( 2 min )

Owing to their superior modeling capabilities, gated Recurrent Neural Networks, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. This paper aims to discuss how these networks can be adopted for the synthesis of Internal Model Control (IMC) architectures. To this end, first a gated recurrent network is used to learn a model of the unknown input-output stable plant. Then, a controller gated recurrent network is trained to approximate the model inverse. The stability of these networks, ensured by means of a suitable training procedure, allows to guarantee the input-output closed-loop stability. The proposed scheme is able to cope with the saturation of the control variables, and can be deployed on low-power embedded controllers, as it requires limited online computations. The approach is then tested on the Quadruple Tank benchmark system and compared to alternative control laws, resulting in remarkable closed-loop performances.  ( 2 min )

In this paper, we present a novel architecture to realize fine-grained style control on the transformer-based text-to-speech synthesis (TransformerTTS). Specifically, we model the speaking style by extracting a time sequence of local style tokens (LST) from the reference speech. The existing content encoder in TransformerTTS is then replaced by our designed cross-attention blocks for fusion and alignment between content and style. As the fusion is performed along with the skip connection, our cross-attention block provides a good inductive bias to gradually infuse the phoneme representation with a given style. Additionally, we prevent the style embedding from encoding linguistic content by randomly truncating LST during training and using wav2vec 2.0 features. Experiments show that with fine-grained style control, our system performs better in terms of naturalness, intelligibility, and style transferability. Our code and samples are publicly available.  ( 2 min )

This paper investigates the model-based methods in multi-agent reinforcement learning (MARL). We specify the dynamics sample complexity and the opponent sample complexity in MARL, and conduct a theoretic analysis of return discrepancy upper bound. To reduce the upper bound with the intention of low sample complexity during the whole learning process, we propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy Optimization (AORPO). In AORPO, each agent builds its multi-agent environment model, consisting of a dynamics model and multiple opponent models, and trains its policy with the adaptive opponent-wise rollout. We further prove the theoretic convergence of AORPO under reasonable assumptions. Empirical experiments on competitive and cooperative tasks demonstrate that AORPO can achieve improved sample efficiency with comparable asymptotic performance over the compared MARL methods.  ( 2 min )

When designing Convolutional Neural Networks (CNNs), one must select the size\break of the convolutional kernels before training. Recent works show CNNs benefit from different kernel sizes at different layers, but exploring all possible combinations is unfeasible in practice. A more efficient approach is to learn the kernel size during training. However, existing works that learn the kernel size have a limited bandwidth. These approaches scale kernels by dilation, and thus the detail they can describe is limited. In this work, we propose FlexConv, a novel convolutional operation with which high bandwidth convolutional kernels of learnable kernel size can be learned at a fixed parameter cost. FlexNets model long-term dependencies without the use of pooling, achieve state-of-the-art performance on several sequential datasets, outperform recent works with learned kernel sizes, and are competitive with much deeper ResNets on image benchmark datasets. Additionally, FlexNets can be deployed at higher resolutions than those seen during training. To avoid aliasing, we propose a novel kernel parameterization with which the frequency of the kernels can be analytically controlled. Our novel kernel parameterization shows higher descriptive power and faster convergence speed than existing parameterizations. This leads to important improvements in classification accuracy.  ( 2 min )

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restrictive in practice. To remedy such drawbacks, this paper proposes a novel noisy tensor completion model, which complements the incompetence of existing works in handling the degeneration of high-order and noisy observations. Specifically, the tensor ring nuclear norm (TRNN) and least-squares estimator are adopted to regularize the underlying tensor and the observed entries, respectively. In addition, a non-asymptotic upper bound of estimation error is provided to depict the statistical performance of the proposed estimator. Two efficient algorithms are developed to solve the optimization problem with convergence guarantee, one of which is specially tailored to handle large-scale tensors by replacing the minimization of TRNN of the original tensor equivalently with that of a much smaller one in a heterogeneous tensor decomposition framework. Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.  ( 2 min )

Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may enable such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Machine Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and synthesize new designs. Recently, DGMs such as feedforward Neural Networks (NNs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in engineering design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances to benefit researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported the development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion, we identify possible solution pathways as key areas on which to target future work.  ( 2 min )

Modern deep neural networks for classification usually jointly learn a backbone for representation and a linear classifier to output the logit of each class. A recent study has shown a phenomenon called neural collapse that the within-class means of features and the classifier vectors converge to the vertices of a simplex equiangular tight frame (ETF) at the terminal phase of training on a balanced dataset. Since the ETF geometric structure maximally separates the pair-wise angles of all classes in the classifier, it is natural to raise the question, why do we spend an effort to learn a classifier when we know its optimal geometric structure? In this paper, we study the potential of learning a neural network for classification with the classifier randomly initialized as an ETF and fixed during training. Our analytical work based on the layer-peeled model indicates that the feature learning with a fixed ETF classifier naturally leads to the neural collapse state even when the dataset is imbalanced among classes. We further show that in this case the cross entropy (CE) loss is not necessary and can be replaced by a simple squared loss that shares the same global optimality but enjoys a more accurate gradient and better convergence property. Our experimental results show that our method is able to achieve similar performances on image classification for balanced datasets, and bring significant improvements in the long-tailed and fine-grained classification tasks.  ( 2 min )

Deep neural networks may be susceptible to learning spurious correlations that hold on average but not in atypical test samples. As with the recent emergence of vision transformer (ViT) models, it remains underexplored how spurious correlations are manifested in such architectures. In this paper, we systematically investigate the robustness of vision transformers to spurious correlations on three challenging benchmark datasets and compare their performance with popular CNNs. Our study reveals that when pre-trained on a sufficiently large dataset, ViT models are more robust to spurious correlations than CNNs. Key to their success is the ability to generalize better from the examples where spurious correlations do not hold. Further, we perform extensive ablations and experiments to understand the role of the self-attention mechanism in providing robustness under spuriously correlated environments. We hope that our work will inspire future research on further understanding the robustness of ViT models.  ( 2 min )

Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights. However, existing methods measuring the closeness are not very reliable: they are discrete and can take only a few values, and they are path-dependent, i.e., they may change given the same start and end points with different attack paths. In this paper, we propose three types of probabilistic margin (PM), which are continuous and path-independent, for measuring the aforementioned closeness and reweighting adversarial data. Specifically, a PM is defined as the difference between two estimated class-posterior probabilities, e.g., such the probability of the true label minus the probability of the most confusing label given some natural data. Though different PMs capture different geometric properties, all three PMs share a negative correlation with the vulnerability of data: data with larger/smaller PMs are safer/riskier and should have smaller/larger weights. Experiments demonstrate that PMs are reliable measurements and PM-based reweighting methods outperform state-of-the-art methods.  ( 2 min )

The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist. This means that the travel demand across different travel modes could be correlated as one mode may receive demand from or create demand for another mode, not to mention natural correlations between different demand time series due to general demand flow patterns across the network. It is expectable that cross-modal ripple effects become more prevalent, with Mobility as a Service. Therefore, by propagating demand data across modes, a better demand prediction could be obtained. To this end, this study explores various machine learning models and transfer learning strategies for cross-modal demand prediction. The trip data of bike-share, metro, and taxi are processed as the station-level passenger flows, and then the proposed prediction method is tested in the large-scale case studies of Nanjing and Chicago. The results suggest that prediction models with transfer learning perform better than unimodal prediction models. Furthermore, stacked Long Short-Term Memory model performs particularly well in cross-modal demand prediction. These results verify our combined method's forecasting improvement over existing benchmarks and demonstrate the good transferability for cross-modal demand prediction in multiple cities.  ( 2 min )

The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical applicability. To maximize the transfer success rate, adversarial examples should avoid overfitting to the source model, and image augmentation is one of the primary approaches for this. However, prior works utilize simple image transformations such as resizing, which limits input diversity. To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class. Our motivation comes from the humans' superior perception of an image printed on a 3D object. If the image is clear enough, humans can recognize the image content in a variety of viewing conditions. Likewise, if an adversarial example looks like the target class to the model, the model should also classify the rendered image of the 3D object as the target class. The ODI method effectively diversifies the input by leveraging an ensemble of multiple source objects and randomizing viewing conditions. In our experimental results on the ImageNet-Compatible dataset, this method boosts the average targeted attack success rate from 28.3% to 47.0% compared to the state-of-the-art methods. We also demonstrate the applicability of the ODI method to adversarial examples on the face verification task and its superior performance improvement. Our code is available at https://github.com/dreamflake/ODI.  ( 2 min )

We consider an online stochastic game with risk-averse agents whose goal is to learn optimal decisions that minimize the risk of incurring significantly high costs. Specifically, we use the Conditional Value at Risk (CVaR) as a risk measure that the agents can estimate using bandit feedback in the form of the cost values of only their selected actions. Since the distributions of the cost functions depend on the actions of all agents that are generally unobservable, they are themselves unknown and, therefore, the CVaR values of the costs are difficult to compute. To address this challenge, we propose a new online risk-averse learning algorithm that relies on one-point zeroth-order estimation of the CVaR gradients computed using CVaR values that are estimated by appropriately sampling the cost functions. We show that this algorithm achieves sub-linear regret with high probability. We also propose two variants of this algorithm that improve performance. The first variant relies on a new sampling strategy that uses samples from the previous iteration to improve the estimation accuracy of the CVaR values. The second variant employs residual feedback that uses CVaR values from the previous iteration to reduce the variance of the CVaR gradient estimates. We theoretically analyze the convergence properties of these variants and illustrate their performance on an online market problem that we model as a Cournot game.  ( 2 min )

Real-world data often exhibit imbalanced label distributions. Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution. However, natural data can originate from distinct domains, where a minority class in one domain could have abundant instances from other domains. We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addresses label imbalance, domain shift, and divergent label distributions across domains, and generalizes to all domain-class pairs. We first develop the domain-class transferability graph, and show that such transferability governs the success of learning in MDLT. We then propose BoDA, a theoretically grounded learning strategy that tracks the upper bound of transferability statistics, and ensures balanced alignment and calibration across imbalanced domain-class distributions. We curate five MDLT benchmarks based on widely-used multi-domain datasets, and compare BoDA to twenty algorithms that span different learning strategies. Extensive and rigorous experiments verify the superior performance of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on Domain Generalization benchmarks, improving generalization to unseen domains. Code and data are available at https://github.com/YyzHarry/multi-domain-imbalance.  ( 2 min )

Communications are realized as a result of successive decisions at the physical layer, from modulation selection to multi-antenna strategy, and each decision affects the performance of the communication systems. Future communication systems must include extensive capabilities as they will encompass a wide variety of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements, as they are often based on impractical and oversimplifying assumptions that result in a trade-off between complexity and efficiency. By leveraging past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and enable rapid response even under exceptional circumstances. The corresponding design solutions should evolve following the lines of learning-driven paradigms that offer more autonomy and robustness. This evolution must take place by considering the facts of real-world systems and without restraining assumptions. In this paper, the common assumptions in the physical layer are presented to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering the implementation steps and challenges. Furthermore, these issues are discussed through a real-time case study using software-defined radio nodes to demonstrate the potential performance improvement. A cyber-physical framework is presented to incorporate future remedies.  ( 2 min )

Large-scale crop type classification is a task at the core of remote sensing efforts with applications of both economic and ecological importance. Current state-of-the-art deep learning methods are based on self-attention and use satellite image time series (SITS) to discriminate crop types based on their unique growth patterns. However, existing methods generalize poorly to regions not seen during training mainly due to not being robust to temporal shifts of the growing season caused by variations in climate. To this end, we propose Thermal Positional Encoding (TPE) for attention-based crop classifiers. Unlike previous positional encoding based on calendar time (e.g. day-of-year), TPE is based on thermal time, which is obtained by accumulating daily average temperatures over the growing season. Since crop growth is directly related to thermal time, but not calendar time, TPE addresses the temporal shifts between different regions to improve generalization. We propose multiple TPE strategies, including learnable methods, to further improve results compared to the common fixed positional encodings. We demonstrate our approach on a crop classification task across four different European regions, where we obtain state-of-the-art generalization results.  ( 2 min )

Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studies focus on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Firstly, only collaborative item-item relationships are implicitly modeled through high-order item-user-item co-occurrences. We argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and assist the recommender models to comprehensively discover candidate items. Secondly, previous studies disregard the fine-grained multimodal fusion. Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships.To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity). To be specific, we devise a novel modality-aware structure learning module, which learns item-item relationships for each modality. Based on the learned modality-aware latent item relationships, we perform graph convolutions that explicitly inject item affinities to modality-aware item representations. Then, we design a novel contrastive method to fuse multimodal features. These enriched item representations can be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on real-world datasets demonstrate the superiority of our method over state-of-the-art baselines.  ( 2 min )

The ability to predict the future trajectory of a patient is a key step toward the development of therapeutics for complex diseases such as Alzheimer's disease (AD). However, most machine learning approaches developed for prediction of disease progression are either single-task or single-modality models, which can not be directly adopted to our setting involving multi-task learning with high dimensional images. Moreover, most of those approaches are trained on a single dataset (i.e. cohort), which can not be generalized to other cohorts. We propose a novel multimodal multi-task deep learning model to predict AD progression by analyzing longitudinal clinical and neuroimaging data from multiple cohorts. Our proposed model integrates high dimensional MRI features from a 3D convolutional neural network with other data modalities, including clinical and demographic information, to predict the future trajectory of patients. Our model employs an adversarial loss to alleviate the study-specific imaging bias, in particular the inter-study domain shifts. In addition, a Sharpness-Aware Minimization (SAM) optimization technique is applied to further improve model generalization. The proposed model is trained and tested on various datasets in order to evaluate and validate the results. Our results showed that 1) our model yields significant improvement over the baseline models, and 2) models using extracted neuroimaging features from 3D convolutional neural network outperform the same models when applied to MRI-derived volumetric features.  ( 2 min )

End-to-end Network has become increasingly important in multi-tasking. One prominent example of this is the growing significance of a driving perception system in autonomous driving. This paper systematically studies an end-to-end perception network for multi-tasking and proposes several key optimizations to improve accuracy. First, the paper proposes efficient segmentation head and box/class prediction networks based on weighted bidirectional feature network. Second, the paper proposes automatically customized anchor for each level in the weighted bidirectional feature network. Third, the paper proposes an efficient training loss function and training strategy to balance and optimize network. Based on these optimizations, we have developed an end-to-end perception network to perform multi-tasking, including traffic object detection, drivable area segmentation and lane detection simultaneously, called HybridNets, which achieves better accuracy than prior art. In particular, HybridNets achieves 77.3 mean Average Precision on Berkeley DeepDrive Dataset, outperforms lane detection with 31.6 mean Intersection Over Union with 12.83 million parameters and 15.6 billion floating-point operations. In addition, it can perform visual perception tasks in real-time and thus is a practical and accurate solution to the multi-tasking problem. Code is available at https://github.com/datvuthanh/HybridNets.  ( 2 min )

Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention. However, due to their complex architectures and large numbers of parameters, measuring the generalization ability of specific DNN models remains an open challenge. In this paper, we propose to use multiple factors to measure and rank the relative generalization of DNNs based on a new concept of confidence dimension (CD). Furthermore, we provide a feasible framework in our CD to theoretically calculate the upper bound of generalization based on the conventional Vapnik-Chervonenk dimension (VC-dimension) and Hoeffding's inequality. Experimental results on image classification and object detection demonstrate that our CD can reflect the relative generalization ability for different DNNs. In addition to full-precision DNNs, we also analyze the generalization ability of binary neural networks (BNNs), whose generalization ability remains an unsolved problem. Our CD yields a consistent and reliable measure and ranking for both full-precision DNNs and BNNs on all the tasks.  ( 2 min )

Finetuning a pretrained backbone in the encoder part of an image transformer network has been the traditional approach for the semantic segmentation task. However, such an approach leaves out the semantic context that an image provides during the encoding stage. This paper argues that incorporating semantic information of the image into pretrained hierarchical transformer-based backbones while finetuning improves the performance considerably. To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation. In addition, we use a lightweight semantic decoder during training to provide supervision to the intermediate semantic prior maps at every stage. Our experiments demonstrate that incorporating semantic priors enhances the performance of the established hierarchical encoders with a slight increase in the number of FLOPs. We provide empirical proof by integrating SeMask into Swin Transformer and Mix Transformer backbones as our encoder paired with different decoders. Our framework achieves a new state-of-the-art of 58.22% mIoU on the ADE20K dataset and improvements of over 3% in the mIoU metric on the Cityscapes dataset. The code and checkpoints are publicly available at https://github.com/Picsart-AI-Research/SeMask-Segmentation .  ( 2 min )

Non-rigid registration computes an alignment between a source surface with a target surface in a non-rigid manner. In the past decade, with the advances in 3D sensing technologies that can measure time-varying surfaces, non-rigid registration has been applied for the acquisition of deformable shapes and has a wide range of applications. This survey presents a comprehensive review of non-rigid registration methods for 3D shapes, focusing on techniques related to dynamic shape acquisition and reconstruction. In particular, we review different approaches for representing the deformation field, and the methods for computing the desired deformation. Both optimization-based and learning-based methods are covered. We also review benchmarks and datasets for evaluating non-rigid registration methods, and discuss potential future research directions.  ( 2 min )

With recent progress in joint modeling of visual and textual representations, Vision-Language Pretraining (VLP) has achieved impressive performance on many multimodal downstream tasks. However, the requirement for expensive annotations including clean image captions and regional labels limits the scalability of existing approaches, and complicates the pretraining procedure with the introduction of multiple dataset-specific objectives. In this work, we relax these constraints and present a minimalist pretraining framework, named Simple Visual Language Model (SimVLM). Unlike prior work, SimVLM reduces the training complexity by exploiting large-scale weak supervision, and is trained end-to-end with a single prefix language modeling objective. Without utilizing extra data or task-specific customization, the resulting model significantly outperforms previous pretraining methods and achieves new state-of-the-art results on a wide range of discriminative and generative vision-language benchmarks, including VQA (+3.74% vqa-score), NLVR2 (+1.17% accuracy), SNLI-VE (+1.37% accuracy) and image captioning tasks (+10.1% average CIDEr score). Furthermore, we demonstrate that SimVLM acquires strong generalization and transfer ability, enabling zero-shot behavior including open-ended visual question answering and cross-modality transfer.  ( 2 min )

In recent years, researchers have extensively studied adversarial robustness in a variety of threat models, including L_0, L_1, L_2, and L_infinity-norm bounded adversarial attacks. However, attacks bounded by fractional L_p "norms" (quasi-norms defined by the L_p distance with 0<p<1) have yet to be thoroughly considered. We proactively propose a defense with several desirable properties: it provides provable (certified) robustness, scales to ImageNet, and yields deterministic (rather than high-probability) certified guarantees when applied to quantized data (e.g., images). Our technique for fractional L_p robustness constructs expressive, deep classifiers that are globally Lipschitz with respect to the L_p^p metric, for any 0<p<1. However, our method is even more general: we can construct classifiers which are globally Lipschitz with respect to any metric defined as the sum of concave functions of components. Our approach builds on a recent work, Levine and Feizi (2021), which provides a provable defense against L_1 attacks. However, we demonstrate that our proposed guarantees are highly non-vacuous, compared to the trivial solution of using (Levine and Feizi, 2021) directly and applying norm inequalities. Code is available at https://github.com/alevine0/fractionalLpRobustness.  ( 2 min )

Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times Dense Matrix Multiplication (SpMM) appear in diverse settings, such as collaborative filtering, document clustering, and graph embedding. Frequently, the SDDMM output becomes the input sparse matrix for a subsequent SpMM operation. Existing work has focused on shared memory parallelization of these primitives. While there has been extensive analysis of communication-minimizing distributed 1.5D algorithms for SpMM, no such analysis exists for SDDMM or the back-to-back sequence of SDDMM and SpMM, termed FusedMM. We show that distributed memory 1.5D and 2.5D algorithms for SpMM can be converted to algorithms for SDDMM with identical communication costs and input / output data layouts. Further, we give two communication-eliding strategies to reduce costs further for FusedMM kernels: either reusing the replication of an input dense matrix for the SDDMM and SpMM in sequence, or fusing the local SDDMM and SpMM kernels. We benchmark FusedMM algorithms on Cori, a Cray XC40 at LBNL, using Erdos-Renyi random matrices and large real-world sparse matrices. On 256 nodes with 68 cores each, 1.5D FusedMM algorithms using either communication eliding approach can save at least 30% of time spent exclusively in communication compared to executing a distributed-memory SpMM and SDDMM kernel in sequence. On real-world matrices with hundreds of millions of edges, all of our algorithms exhibit at least a 10x speedup over the SpMM algorithm in PETSc. On these matrices, our communication-eliding techniques exhibit runtimes up to 1.6 times faster than an unoptimized sequence of SDDMM and SpMM. We embed and test the scaling of our algorithms in real-world applications, including collaborative filtering via alternating-least-squares and inference for attention-based graph neural networks.  ( 2 min )

We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems. Two distinctive features of our approach are that (i) projections or optimizations over the entire feasible set are avoided, in stark contrast to projected gradient methods or the Frank-Wolfe method, and (ii) iterates are allowed to become infeasible, which differs from active set or feasible direction methods, where the descent motion stops as soon as a new constraint is encountered. The resulting algorithmic procedure is simple to implement even when constraints are nonlinear, and is suitable for large-scale constrained optimization problems in which the feasible set fails to have a simple structure. The key underlying idea is that constraints are expressed in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. In particular, this means that at each iteration only constraints that are violated are taken into account. The result is a simplified suite of algorithms and an expanded range of possible applications in machine learning.  ( 2 min )

Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring rapid and often intensive medical treatment. Such a condition is traditionally diagnosed by highly-trained specialists analyzing computed tomography (CT) scan of the patient and identifying the location and type of hemorrhage if one exists. We propose a neural network approach to find and classify the condition based upon the CT scan. The model architecture implements a time distributed convolutional network. We observed accuracy above 92% from such an architecture, provided enough data. We propose further extensions to our approach involving the deployment of federated learning. This would be helpful in pooling learned parameters without violating the inherent privacy of the data involved.  ( 2 min )

Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO. This gap exists largely because commonly used random crop augmentations obtain semantically inconsistent content in crowded scene images of diverse objects. Previous works use preprocessing pipelines to localize salient objects for improved cropping, but an end-to-end solution is still elusive. In this work, we propose a framework which accomplishes this goal via joint learning of representations and segmentation. We leverage segmentation masks to train a model with a mask-dependent contrastive loss, and use the partially trained model to bootstrap better masks. By iterating between these two components, we ground the contrastive updates in segmentation information, and simultaneously improve segmentation throughout pretraining. Experiments show our representations transfer robustly to downstream tasks in classification, detection and segmentation.  ( 2 min )

Some examples are easier for humans to classify than others. The same should be true for deep neural networks (DNNs). We use the term example perplexity to refer to the level of difficulty of classifying an example. In this paper, we propose a method to measure the perplexity of an example and investigate what factors contribute to high example perplexity. The related codes and resources are available at https://github.com/vaynexie/Example-Perplexity.  ( 2 min )

We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset of them) are being modified. We show that with small-to-medium training data, applying BitFit on pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. For larger data, the method is competitive with other sparse fine-tuning methods. Besides their practical utility, these findings are relevant for the question of understanding the commonly-used process of finetuning: they support the hypothesis that finetuning is mainly about exposing knowledge induced by language-modeling training, rather than learning new task-specific linguistic knowledge.  ( 2 min )

Powerful priors allow us to perform inference with insufficient information. In this paper, we propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape completion, reconstruction, and generation. We model the distribution over 3D shapes as a non-sequential autoregressive distribution over a discretized, low-dimensional, symbolic grid-like latent representation of 3D shapes. This enables us to represent distributions over 3D shapes conditioned on information from an arbitrary set of spatially anchored query locations and thus perform shape completion in such arbitrary settings (e.g., generating a complete chair given only a view of the back leg). We also show that the learned autoregressive prior can be leveraged for conditional tasks such as single-view reconstruction and language-based generation. This is achieved by learning task-specific naive conditionals which can be approximated by light-weight models trained on minimal paired data. We validate the effectiveness of the proposed method using both quantitative and qualitative evaluation and show that the proposed method outperforms the specialized state-of-the-art methods trained for individual tasks. The project page with code and video visualizations can be found at https://yccyenchicheng.github.io/AutoSDF/.  ( 2 min )

To compare alternative taxi schedules and to compute them, as well as to provide insights into an upcoming taxi trip to drivers and passengers, the duration of a trip or its Estimated Time of Arrival (ETA) is predicted. To reach a high prediction precision, machine learning models for ETA are state of the art. One yet unexploited option to further increase prediction precision is to combine multiple ETA models into an ensemble. While an increase of prediction precision is likely, the main drawback is that the predictions made by such an ensemble become less transparent due to the sophisticated ensemble architecture. One option to remedy this drawback is to apply eXplainable Artificial Intelligence (XAI). The contribution of this paper is three-fold. First, we combine multiple machine learning models from our previous work for ETA into a two-level ensemble model - a stacked ensemble model - which on its own is novel; therefore, we can outperform previous state-of-the-art static route-free ETA approaches. Second, we apply existing XAI methods to explain the first- and second-level models of the ensemble. Third, we propose three joining methods for combining the first-level explanations with the second-level ones. Those joining methods enable us to explain stacked ensembles for regression tasks. An experimental evaluation shows that the ETA models correctly learned the importance of those input features driving the prediction.  ( 2 min )

Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive to obtain through fully manual segmentation methods. Additionally, lung lesions have large inter-patient variations, with some pathologies having similar visual appearance as healthy lung tissues. This poses a challenge when applying existing semi-automatic interactive segmentation techniques for data labelling. To address these challenges, we propose an efficient convolutional neural networks (CNNs) that can be learned online while the annotator provides scribble-based interaction. To accelerate learning from only the samples labelled through user-interactions, a patch-based approach is used for training the network. Moreover, we use weighted cross-entropy loss to address the class imbalance that may result from user-interactions. During online inference, the learned network is applied to the whole input volume using a fully convolutional approach. We compare our proposed method with state-of-the-art using synthetic scribbles and show that it outperforms existing methods on the task of annotating lung lesions associated with COVID-19, achieving 16% higher Dice score while reducing execution time by 3$\times$ and requiring 9000 lesser scribbles-based labelled voxels. Due to the online learning aspect, our approach adapts quickly to user input, resulting in high quality segmentation labels. Source code for ECONet is available at: https://github.com/masadcv/ECONet-MONAILabel.  ( 2 min )

Recently introduced instruction-paradigm empowers non-expert users to leverage NLP resources by defining a new task in natural language. Instruction-tuned models have significantly outperformed multitask learning models (without instruction); however they are far from state of the art task specific models. Conventional approaches to improve model performance via creating large datasets with lots of task instances or architectural/training changes in model may not be feasible for non-expert users. However, they can write alternate instructions to represent an instruction task. Is Instruction-augumentation helpful? We augment a subset of tasks in NATURAL INSTRUCTIONS with additional instructions and find that these significantly improve model performance (upto 35%) specially in low-data regime. Our results indicate that an additional instruction can be equivalent to ~40 instances on average across our evaluation tasks.  ( 2 min )

In this paper, to reduce the congestion rate at the city center and increase the quality of experience (QoE) of each user, the framework of long-range autonomous valet parking (LAVP) is presented, where an Autonomous Vehicle (AV) is deployed in the city, which can pick up, drop off users at their required spots, and then drive to the car park out of city center autonomously. In this framework, we aim to minimize the overall distance of the AV, while guarantee all users are served, i.e., picking up, and dropping off users at their required spots through optimizing the path planning of the AV and number of serving time slots. To this end, we first propose a learning based algorithm, which is named as Double-Layer Ant Colony Optimization (DL-ACO) algorithm to solve the above problem in an iterative way. Then, to make the real-time decision, while consider the dynamic environment (i.e., the AV may pick up and drop off users from different locations), we further present a deep reinforcement learning (DRL) based algorithm, which is known as deep Q network (DQN). The experimental results show that the DL-ACO and DQN-based algorithms both achieve the considerable performance.  ( 2 min )

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous success stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial role of AI research in this unprecedented battle. We touch on areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potential of AI and enhancing its capability and power in the pandemic battle are thoroughly discussed. We identify 13 groups of problems related to the COVID-19 pandemic and highlight promising AI methods and tools that can be used to address these problems. It is envisaged that this study will provide AI researchers and the wider community with an overview of the current status of AI applications, and motivate researchers to harness AI's potential in the fight against COVID-19.  ( 3 min )

The unprecedented increase of commercial airlines and private jets over the next ten years presents a challenge for air traffic control. Precise flight trajectory prediction is of great significance in air transportation management, which contributes to the decision-making for safe and orderly flights. Existing research and application mainly focus on the sequence generation based on historical trajectories, while the aircraft-aircraft interactions in crowded airspace especially the airspaces near busy airports have been largely ignored. On the other hand, there are distinct characteristics of aerodynamics for different flight phases, and the trajectory may be affected by various uncertainties such as weather and advisories from air traffic controllers. However, there is no literature fully considers all these issues. Therefore, we proposed a phased flight trajectory prediction framework. Multi-source and multi-modal datasets have been analyzed and mined using variants of recurrent neural network (RNN) mixture. To be specific, we first introduce spatio temporal graphs into the low-altitude airway prediction problem, and the motion constraints of an aircraft are embedded to the inference process for reliable forecasting results. In the en-route phase, the dual attention mechanism is employed to adaptively extract much more important features from overall datasets to learn the hidden patterns in dynamical environments. The experimental results demonstrate our proposed framework can outperform state-of-the-art methods for flight trajectory prediction for large passenger/transport airplanes.  ( 2 min )

The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network model that is trained to learn the mapping from the raw density field containing each halo to the dark matter density profile. We show that the model recovers the widely-used Navarro-Frenk-White (NFW) profile out to the virial radius, and can additionally describe the variability in the outer profile of the halos. The neural network architecture consists of a supervised encoder-decoder framework, which first compresses the density inputs into a low-dimensional latent representation, and then outputs $\rho(r)$ for any desired value of radius $r$. The latent representation contains all the information used by the model to predict the density profiles. This allows us to interpret the latent representation by quantifying the mutual information between the representation and the halos' ground-truth density profiles. A two-dimensional representation is sufficient to accurately model the density profiles up to the virial radius; however, a three-dimensional representation is required to describe the outer profiles beyond the virial radius. The additional dimension in the representation contains information about the infalling material in the outer profiles of dark matter halos, thus discovering the splashback boundary of halos without prior knowledge of the halos' dynamical history.  ( 2 min )

World Input-Output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (part of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and Matrix Completion (MC) with a LASSO-like nuclear norm penalty, to impute missing entries of a partially unknown I/O matrix. Through simulations based on synthetic matrices we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input-Output Database (WIOD) tables - which are an example of industry-by-industry I/O tables - is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them.  ( 2 min )

We study stochastic monotone inclusion problems, which widely appear in machine learning applications, including robust regression and adversarial learning. We propose novel variants of stochastic Halpern iteration with recursive variance reduction. In the cocoercive -- and more generally Lipschitz-monotone -- setup, our algorithm attains $\epsilon$ norm of the operator with $\mathcal{O}(\frac{1}{\epsilon^3})$ stochastic operator evaluations, which significantly improves over state of the art $\mathcal{O}(\frac{1}{\epsilon^4})$ stochastic operator evaluations required for existing monotone inclusion solvers applied to the same problem classes. We further show how to couple one of the proposed variants of stochastic Halpern iteration with a scheduled restart scheme to solve stochastic monotone inclusion problems with ${\mathcal{O}}(\frac{\log(1/\epsilon)}{\epsilon^2})$ stochastic operator evaluations under additional sharpness or strong monotonicity assumptions. Finally, we argue via reductions between different problem classes that our stochastic oracle complexity bounds are tight up to logarithmic factors in terms of their $\epsilon$-dependence.  ( 2 min )

Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. This setting is particularly well-suited for continuous control robotic applications for which online data collection based on trial-and-error is costly and potentially unsafe. In practice, offline datasets are often heterogeneous, i.e., collected in a variety of scenarios, such as data from several human demonstrators or from policies that act with different purposes. Unfortunately, such datasets can exacerbate the distribution shift between the behavior policy underlying the data and the optimal policy to be learned, leading to poor performance. To address this challenge, we propose to leverage latent-variable policies that can represent a broader class of policy distributions, leading to better adherence to the training data distribution while maximizing reward via a policy over the latent variable. As we empirically show on a range of simulated locomotion, navigation, and manipulation tasks, our method referred to as latent-variable advantage-weighted policy optimization (LAPO), improves the average performance of the next best-performing offline reinforcement learning methods by 49% on heterogeneous datasets, and by 8% on datasets with narrow and biased distributions.  ( 2 min )

Speech Emotion Recognition (SER) application is frequently associated with privacy concerns as it often acquires and transmits speech data at the client-side to remote cloud platforms for further processing. These speech data can reveal not only speech content and affective information but the speaker's identity, demographic traits, and health status. Federated learning (FL) is a distributed machine learning algorithm that coordinates clients to train a model collaboratively without sharing local data. This algorithm shows enormous potential for SER applications as sharing raw speech or speech features from a user's device is vulnerable to privacy attacks. However, a major challenge in FL is limited availability of high-quality labeled data samples. In this work, we propose a semi-supervised federated learning framework, Semi-FedSER, that utilizes both labeled and unlabeled data samples to address the challenge of limited labeled data samples in FL. We show that our Semi-FedSER can generate desired SER performance even when the local label rate l=20 using two SER benchmark datasets: IEMOCAP and MSP-Improv.  ( 2 min )

A major limitation in applying deep learning to artificial intelligence (AI) systems is the scarcity of high-quality curated datasets. We investigate strong augmentation based self-supervised learning (SSL) techniques to address this problem. Using breast cancer detection as an example, we first identify a mammogram-specific transformation paradigm and then systematically compare four recent SSL methods representing a diversity of approaches. We develop a method to convert a pretrained model from making predictions on uniformly tiled patches to whole images, and an attention-based pooling method that improves the classification performance. We found that the best SSL model substantially outperformed the baseline supervised model. The best SSL model also improved the data efficiency of sample labeling by nearly 4-fold and was highly transferrable from one dataset to another. SSL represents a major breakthrough in computer vision and may help the AI for medical imaging field to shift away from supervised learning and dependency on scarce labels.  ( 2 min )

HOLODEC, an airborne cloud particle imager, captures holographic images of a fixed volume of cloud to characterize the types and sizes of cloud particles, such as water droplets and ice crystals. Cloud particle properties include position, diameter, and shape. We present a hologram processing algorithm, HolodecML, that utilizes a neural segmentation model, GPUs, and computational parallelization. HolodecML is trained using synthetically generated holograms based on a model of the instrument, and predicts masks around particles found within reconstructed images. From these masks, the position and size of the detected particles can be characterized in three dimensions. In order to successfully process real holograms, we find we must apply a series of image corrupting transformations and noise to the synthetic images used in training. In this evaluation, HolodecML had comparable position and size estimation performance to the standard processing method, but improved particle detection by nearly 20\% on several thousand manually labeled HOLODEC images. However, the improvement only occurred when image corruption was performed on the simulated images during training, thereby mimicking non-ideal conditions in the actual probe. The trained model also learned to differentiate artifacts and other impurities in the HOLODEC images from the particles, even though no such objects were present in the training data set, while the standard processing method struggled to separate particles from artifacts. The novelty of the training approach, which leveraged noise as a means for parameterizing non-ideal aspects of the HOLODEC detector, could be applied in other domains where the theoretical model is incapable of fully describing the real-world operation of the instrument and accurate truth data required for supervised learning cannot be obtained from real-world observations.  ( 2 min )

We show that the fundamental tasks of sorting lists and building search trees or heaps can be modeled as quadratic unconstrained binary optimization problems (QUBOs). The idea is to understand these tasks as permutation problems and to devise QUBOs whose solutions represent appropriate permutation matrices. We discuss how to construct such QUBOs and how to solve them using Hopfield nets or adiabatic) quantum computing. In short, we show that neurocomputing methods or quantum computers can solve problems usually associated with abstract data structures.  ( 2 min )

Communication is an effective mechanism for coordinating the behavior of multiple agents. In the field of multi-agent reinforcement learning, agents can improve the overall learning performance and achieve their objectives by communication. Moreover, agents can communicate various types of messages, either to all agents or to specific agent groups, and through specific channels. With the growing body of research work in MARL with communication (Comm-MARL), there is lack of a systematic and structural approach to distinguish and classify existing Comm-MARL systems. In this paper, we survey recent works in the Comm-MARL field and consider various aspects of communication that can play a role in the design and development of multi-agent reinforcement learning systems. With these aspects in mind, we propose several dimensions along which Comm-MARL systems can be analyzed, developed, and compared.  ( 2 min )

Data is fundamental to machine learning-based products and services and is considered strategic due to its externalities for businesses, governments, non-profits, and more generally for society. It is renowned that the value of organizations (businesses, government agencies and programs, and even industries) scales with the volume of available data. What is often less appreciated is that the data value in making useful organizational predictions will range widely and is prominently a function of data characteristics and underlying algorithms. In this research, our goal is to study how the value of data changes over time and how this change varies across contexts and business areas (e.g. next word prediction in the context of history, sports, politics). We focus on data from Reddit.com and compare the value's time-dependency across various Reddit topics (Subreddits). We make this comparison by measuring the rate at which user-generated text data loses its relevance to the algorithmic prediction of conversations. We show that different subreddits have different rates of relevance decline over time. Relating the text topics to various business areas of interest, we argue that competing in a business area in which data value decays rapidly alters strategies to acquire competitive advantage. When data value decays rapidly, access to a continuous flow of data will be more valuable than access to a fixed stock of data. In this kind of setting, improving user engagement and increasing user-base help creating and maintaining a competitive advantage.  ( 2 min )

This paper presents a motion data augmentation scheme incorporating motion synthesis encouraging diversity and motion correction imposing physical plausibility. This motion synthesis consists of our modified Variational AutoEncoder (VAE) and Inverse Kinematics (IK). In this VAE, our proposed sampling-near-samples method generates various valid motions even with insufficient training motion data. Our IK-based motion synthesis method allows us to generate a variety of motions semi-automatically. Since these two schemes generate unrealistic artifacts in the synthesized motions, our motion correction rectifies them. This motion correction scheme consists of imitation learning with physics simulation and subsequent motion debiasing. For this imitation learning, we propose the PD-residual force that significantly accelerates the training process. Furthermore, our motion debiasing successfully offsets the motion bias induced by imitation learning to maximize the effect of augmentation. As a result, our method outperforms previous noise-based motion augmentation methods by a large margin on both Recurrent Neural Network-based and Graph Convolutional Network-based human motion prediction models. The code is available at {\rm \url{https://github.com/meaten/MotionAug}}.  ( 2 min )

De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of the represetative models. In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing even with missing signal peaks or large number of noisy peaks in tandem mass spectrometry data. It is showed that, for the same test set of DeepNovoV2, the DePS model achieved excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino acid precision and peptide recall respectively. Furthermore, the results suggested that DePS outperforms DeepNovoV2 on the cross species dataset.  ( 2 min )

Physics-informed Neural Network (PINN) is a promising tool that has been applied in a variety of physical phenomena described by partial differential equations (PDE). However, it has been observed that PINNs are difficult to train in certain "stiff" problems, which include various nonlinear hyperbolic PDEs that display shocks in their solutions. Recent studies added a diffusion term to the PDE, and an artificial viscosity (AV) value was manually tuned to allow PINNs to solve these problems. In this paper, we propose three approaches to address this problem, none of which rely on an a priori definition of the artificial viscosity value. The first method learns a global AV value, whereas the other two learn localized AV values around the shocks, by means of a parametrized AV map or a residual-based AV map. We applied the proposed methods to the inviscid Burgers equation and the Buckley-Leverett equation, the latter being a classical problem in Petroleum Engineering. The results show that the proposed methods are able to learn both a small AV value and the accurate shock location and improve the approximation error over a nonadaptive global AV alternative method.  ( 2 min )

Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain open questions. Recent literature suggests that robust networks with good generalization properties tend to be biased towards processing low frequencies in images. To explore the frequency bias hypothesis further, we develop an algorithm that allows us to learn modulatory masks highlighting the essential input frequencies needed for preserving a trained network's performance. We achieve this by imposing invariance in the loss with respect to such modulations in the input frequencies. We first use our method to test the low-frequency preference hypothesis of adversarially trained or data-augmented networks. Our results suggest that adversarially robust networks indeed exhibit a low-frequency bias but we find this bias is also dependent on directions in frequency space. However, this is not necessarily true for other types of data augmentation. Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place. Surprisingly, images seen through these modulatory masks are not recognizable and resemble texture-like patterns.  ( 2 min )

We develop a symbolic regression framework for extracting the governing mathematical expressions from observed data. The evolutionary approach, faiGP, is designed to leverage the properties of a function algebra that have been encoded into a grammar, providing a theoretical guarantee of universal approximation and a way to minimize bloat. In this framework, the choice of operators of the grammar may be informed by a physical theory or symmetry considerations. Since there is currently no theory that can derive the 'constants of nature', an empirical investigation on extracting these coefficients from an evolutionary process is of methodological interest. We quantify the impact of different types of regularizers, including a diversity metric adapted from studies of the transcriptome and a complexity measure, on the performance of the framework. Our implementation, which leverages neural networks and a genetic programmer, generates non-trivial symbolically equivalent expressions ("Ramanujan expressions") or approximations with potentially interesting numerical applications. To illustrate the framework, a model of ligand-receptor binding kinetics, including an account of gene regulation by transcription factors, and a model of the regulatory range of the cistrome from omics data are presented. This study has important implications on the development of data-driven methodologies for the discovery of governing equations in experimental data derived from new sensing systems and high-throughput screening technologies.  ( 2 min )

The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').  ( 2 min )

Access to dermatological care is a major issue, with an estimated 3 billion people lacking access to care globally. Artificial intelligence (AI) may aid in triaging skin diseases. However, most AI models have not been rigorously assessed on images of diverse skin tones or uncommon diseases. To ascertain potential biases in algorithm performance in this context, we curated the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. Using this dataset of 656 images, we show that state-of-the-art dermatology AI models perform substantially worse on DDI, with receiver operator curve area under the curve (ROC-AUC) dropping by 27-36 percent compared to the models' original test results. All the models performed worse on dark skin tones and uncommon diseases, which are represented in the DDI dataset. Additionally, we find that dermatologists, who typically provide visual labels for AI training and test datasets, also perform worse on images of dark skin tones and uncommon diseases compared to ground truth biopsy annotations. Finally, fine-tuning AI models on the well-characterized and diverse DDI images closed the performance gap between light and dark skin tones. Moreover, algorithms fine-tuned on diverse skin tones outperformed dermatologists on identifying malignancy on images of dark skin tones. Our findings identify important weaknesses and biases in dermatology AI that need to be addressed to ensure reliable application to diverse patients and diseases.  ( 2 min )

Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications. One ab initio method for solving the electronic Schr\"odinger equation that scales favorably for large systems and whose accuracy is limited only by the choice of wavefunction ansatz employed is variational quantum Monte Carlo (QMC). The recently introduced deep QMC approach, using a new class of ansatzes represented by deep neural networks, has been shown to generate nearly exact ground-state solutions for molecules containing up to a few dozen electrons, with the potential to scale to much larger systems where other highly accurate methods are not feasible. In this paper, we advance one such ansatz (PauliNet) to compute electronic excited states through a simple variational procedure. We demonstrate our method on a variety of small atoms and molecules where we consistently achieve high accuracy for low-lying states. To highlight the method's potential for larger systems, we show that for the benzene molecule, PauliNet is on par with significantly more expensive high-level electronic structure methods in terms of the excitation energy and outperforms them in terms of absolute energies.  ( 2 min )

Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the parameters of a heteroscedastic Gaussian distribution by maximizing the logarithm of the likelihood function under the observed data. In this work, we examine this approach and identify potential hazards associated with the use of log-likelihood in conjunction with gradient-based optimizers. First, we present a synthetic example illustrating how this approach can lead to very poor but stable parameter estimates. Second, we identify the culprit to be the log-likelihood loss, along with certain conditions that exacerbate the issue. Third, we present an alternative formulation, termed $\beta$-NLL, in which each data point's contribution to the loss is weighted by the $\beta$-exponentiated variance estimate. We show that using an appropriate $\beta$ largely mitigates the issue in our illustrative example. Fourth, we evaluate this approach on a range of domains and tasks and show that it achieves considerable improvements and performs more robustly concerning hyperparameters, both in predictive RMSE and log-likelihood criteria.  ( 2 min )

Searching for the architecture cells is a dominant paradigm in NAS. However, little attention has been devoted to the analysis of the cell-based search spaces even though it is highly important for the continual development of NAS. In this work, we conduct an empirical post-hoc analysis of architectures from the popular cell-based search spaces and find that the existing search spaces contain a high degree of redundancy: the architecture performance is minimally sensitive to changes at large parts of the cells, and universally adopted designs, like the explicit search for a reduction cell, significantly increase the complexities but have very limited impact on the performance. Across architectures found by a diverse set of search strategies, we consistently find that the parts of the cells that do matter for architecture performance often follow similar and simple patterns. By explicitly constraining cells to include these patterns, randomly sampled architectures can match or even outperform the state of the art. These findings cast doubts into our ability to discover truly novel architectures in the existing cell-based search spaces, and inspire our suggestions for improvement to guide future NAS research. Code is available at https://github.com/xingchenwan/cell-based-NAS-analysis.  ( 2 min )

To compare alternative taxi schedules and to compute them, as well as to provide insights into an upcoming taxi trip to drivers and passengers, the duration of a trip or its Estimated Time of Arrival (ETA) is predicted. To reach a high prediction precision, machine learning models for ETA are state of the art. One yet unexploited option to further increase prediction precision is to combine multiple ETA models into an ensemble. While an increase of prediction precision is likely, the main drawback is that the predictions made by such an ensemble become less transparent due to the sophisticated ensemble architecture. One option to remedy this drawback is to apply eXplainable Artificial Intelligence (XAI). The contribution of this paper is three-fold. First, we combine multiple machine learning models from our previous work for ETA into a two-level ensemble model - a stacked ensemble model - which on its own is novel; therefore, we can outperform previous state-of-the-art static route-free ETA approaches. Second, we apply existing XAI methods to explain the first- and second-level models of the ensemble. Third, we propose three joining methods for combining the first-level explanations with the second-level ones. Those joining methods enable us to explain stacked ensembles for regression tasks. An experimental evaluation shows that the ETA models correctly learned the importance of those input features driving the prediction.  ( 2 min )

The abundance of dark matter subhalos orbiting a host galaxy is a generic prediction of the cosmological framework. It is a promising way to constrain the nature of dark matter. Here we describe the challenges of detecting stars whose phase-space distribution may be perturbed by the passage of dark matter subhalos using a machine learning approach. The training data are three Milky Way-like galaxies and nine synthetic Gaia DR2 surveys derived from these. We first quantify the magnitude of the perturbations in the simulated galaxies using an anomaly detection algorithm. We also estimate the feasibility of this approach in the Gaia DR2-like catalogues by comparing the anomaly detection based approach with a supervised classification. We find that a classification algorithm optimized on about half a billion synthetic star observables exhibits mild but nonzero sensitivity. This classification-based approach is not sufficiently sensitive to pinpoint the exact locations of subhalos in the simulation, as would be expected from the very limited number of subhalos in the detectable region. The enormous size of the Gaia dataset motivates the further development of scalable and accurate computational methods that could be used to select potential regions of interest for dark matter searches to ultimately constrain the Milky Way's subhalo mass function.  ( 2 min )

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restrictive in practice. To remedy such drawbacks, this paper proposes a novel noisy tensor completion model, which complements the incompetence of existing works in handling the degeneration of high-order and noisy observations. Specifically, the tensor ring nuclear norm (TRNN) and least-squares estimator are adopted to regularize the underlying tensor and the observed entries, respectively. In addition, a non-asymptotic upper bound of estimation error is provided to depict the statistical performance of the proposed estimator. Two efficient algorithms are developed to solve the optimization problem with convergence guarantee, one of which is specially tailored to handle large-scale tensors by replacing the minimization of TRNN of the original tensor equivalently with that of a much smaller one in a heterogeneous tensor decomposition framework. Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.  ( 2 min )

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning \mbox{methods} with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions. However, more general situations, such as instance-dependent label noise, have not been fully explored, as scarce studies focus on their label corruption process. By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. The resultant model can be realized by DNNs, where the training procedure is accomplished by employing an alternating optimization algorithm. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness over state-of-the-art counterparts.  ( 2 min )

In a high-dimensional regression framework, we study consequences of the naive two-step procedure where first the dimension of the input variables is reduced and second, the reduced input variables are used to predict the output variable. More specifically we combine principal component analysis (PCA) with kernel regression. In order to analyze the resulting regression errors, a novel stability result of kernel regression with respect to the Wasserstein distance is derived. This allows us to bound errors that occur when perturbed input data is used to fit a kernel function. We combine the stability result with known estimates from the literature on both principal component analysis and kernel regression to obtain convergence rates for the two-step procedure.  ( 2 min )

In many prediction problems, spurious correlations are induced by a changing relationship between the label and a nuisance variable that is also correlated with the covariates. For example, in classifying animals in natural images, the background, which is a nuisance, can predict the type of animal. This nuisance-label relationship does not always hold, and the performance of a model trained under one such relationship may be poor on data with a different nuisance-label relationship. To build predictive models that perform well regardless of the nuisance-label relationship, we develop Nuisance-Randomized Distillation (NURD). We introduce the nuisance-randomized distribution, a distribution where the nuisance and the label are independent. Under this distribution, we define the set of representations such that conditioning on any member, the nuisance and the label remain independent. We prove that the representations in this set always perform better than chance, while representations outside of this set may not. NURD finds a representation from this set that is most informative of the label under the nuisance-randomized distribution, and we prove that this representation achieves the highest performance regardless of the nuisance-label relationship. We evaluate NURD on several tasks including chest X-ray classification where, using non-lung patches as the nuisance, NURD produces models that predict pneumonia under strong spurious correlations.  ( 2 min )

On-demand labor platforms aim to train a skilled workforce to serve its incoming demand for jobs. Since limited jobs are available for training, and it is usually not necessary to train all workers, efficient matching of training jobs requires prioritizing fast learners over slow ones. However, the learning rates of novice workers are unknown, resulting in a tradeoff between exploration (learning the learning rates) and exploitation (training the best workers). Motivated to study this tradeoff, we analyze a novel objective within the stochastic multi-armed bandit framework. Given $K$ arms, instead of maximizing the expected total reward from $T$ pulls (the traditional "sum" objective), we consider the vector of cumulative rewards earned from the $K$ arms at the end of $T$ pulls and aim to maximize the expected highest cumulative reward (the "max" objective). When rewards represent skill increments, this corresponds to the objective of training a single highly skilled worker from a set of novice workers, using a limited supply of training jobs. For this objective, we show that any policy must incur an instance-dependent asymptotic regret of $\Omega(\log T)$ (with a higher instance-dependent constant) and a worst-case regret of $\Omega(K^{1/3}T^{2/3})$. We then design an explore-then-commit policy featuring exploration based on appropriately tuned confidence bounds on the mean reward and an adaptive stopping criterion, which adapts to the problem difficulty and achieves these bounds (up to logarithmic factors). We generalize our algorithmic insights to the problem of maximizing the expected value of the average cumulative reward of the top $m$ arms with the highest cumulative rewards, corresponding to the case where multiple workers must be trained. Our numerical experiments demonstrate the efficacy of our policies compared to several natural alternatives in practical parameter regimes.  ( 3 min )

Denoising diffusion probabilistic models are a promising new class of generative models that are competitive with GANs on perceptual metrics. In this paper, we explore their potential for sequentially generating video. Inspired by recent advances in neural video compression, we use denoising diffusion models to stochastically generate a residual to a deterministic next-frame prediction. We compare this approach to two sequential VAE and two GAN baselines on four datasets, where we test the generated frames for perceptual quality and forecasting accuracy against ground truth frames. We find significant improvements in terms of perceptual quality on all data and improvements in terms of frame forecasting for complex high-resolution videos.  ( 2 min )

The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist. This means that the travel demand across different travel modes could be correlated as one mode may receive demand from or create demand for another mode, not to mention natural correlations between different demand time series due to general demand flow patterns across the network. It is expectable that cross-modal ripple effects become more prevalent, with Mobility as a Service. Therefore, by propagating demand data across modes, a better demand prediction could be obtained. To this end, this study explores various machine learning models and transfer learning strategies for cross-modal demand prediction. The trip data of bike-share, metro, and taxi are processed as the station-level passenger flows, and then the proposed prediction method is tested in the large-scale case studies of Nanjing and Chicago. The results suggest that prediction models with transfer learning perform better than unimodal prediction models. Furthermore, stacked Long Short-Term Memory model performs particularly well in cross-modal demand prediction. These results verify our combined method's forecasting improvement over existing benchmarks and demonstrate the good transferability for cross-modal demand prediction in multiple cities.  ( 2 min )

Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunately, it was shown that these architectures are limited in their expressive power. This paper proposes a novel framework called Equivariant Subgraph Aggregation Networks (ESAN) to address this issue. Our main observation is that while two graphs may not be distinguishable by an MPNN, they often contain distinguishable subgraphs. Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture. We develop novel variants of the 1-dimensional Weisfeiler-Leman (1-WL) test for graph isomorphism, and prove lower bounds on the expressiveness of ESAN in terms of these new WL variants. We further prove that our approach increases the expressive power of both MPNNs and more expressive architectures. Moreover, we provide theoretical results that describe how design choices such as the subgraph selection policy and equivariant neural architecture affect our architecture's expressive power. To deal with the increased computational cost, we propose a subgraph sampling scheme, which can be viewed as a stochastic version of our framework. A comprehensive set of experiments on real and synthetic datasets demonstrates that our framework improves the expressive power and overall performance of popular GNN architectures.  ( 2 min )

In this paper, we analyze the generalization performance of the Iterative Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The parameter estimation and sparsity recovery consistency of IHT has long been known in compressed sensing. From the perspective of statistical learning, another fundamental question is how well the IHT estimation would predict on unseen data. This paper makes progress towards answering this open question by introducing a novel sparse generalization theory for IHT under the notion of algorithmic stability. Our theory reveals that: 1) under natural conditions on the empirical risk function over $n$ samples of dimension $p$, IHT with sparsity level $k$ enjoys an $\mathcal{\tilde O}(n^{-1/2}\sqrt{k\log(n)\log(p)})$ rate of convergence in sparse excess risk; 2) a tighter $\mathcal{\tilde O}(n^{-1/2}\sqrt{\log(n)})$ bound can be established by imposing an additional iteration stability condition on a hypothetical IHT procedure invoked to the population risk; and 3) a fast rate of order $\mathcal{\tilde O}\left(n^{-1}k(\log^3(n)+\log(p))\right)$ can be derived for strongly convex risk function under proper strong-signal conditions. The results have been substantialized to sparse linear regression and sparse logistic regression models to demonstrate the applicability of our theory. Preliminary numerical evidence is provided to confirm our theoretical predictions.  ( 2 min )

A common problem in data science is "use this function defined over this small set to generate predictions over that larger set." Extrapolation, interpolation, statistical inference and forecasting all reduce to this problem. The Kan extension is a powerful tool in category theory that generalizes this notion. In this work we explore several applications of Kan extensions to data science. We begin by deriving a simple classification algorithm as a Kan extension and experimenting with this algorithm on real data. Next, we use the Kan extension to derive a procedure for learning clustering algorithms from labels and explore the performance of this procedure on real data. We then investigate how Kan extensions can be used to learn a general mapping from datasets of labeled examples to functions and to approximate a complex function with a simpler one.  ( 2 min )

In probably approximately correct (PAC) reinforcement learning (RL), an agent is required to identify an $\epsilon$-optimal policy with probability $1-\delta$. While minimax optimal algorithms exist for this problem, its instance-dependent complexity remains elusive in episodic Markov decision processes (MDPs). In this paper, we propose the first (nearly) matching upper and lower bounds on the sample complexity of PAC RL in deterministic episodic MDPs with finite state and action spaces. In particular, our bounds feature a new notion of sub-optimality gap for state-action pairs that we call the deterministic return gap. While our instance-dependent lower bound is written as a linear program, our algorithms are very simple and do not require solving such an optimization problem during learning. Their design and analyses employ novel ideas, including graph-theoretical concepts such as minimum flows and maximum cuts, which we believe to shed new light on this problem.  ( 2 min )

Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention. However, due to their complex architectures and large numbers of parameters, measuring the generalization ability of specific DNN models remains an open challenge. In this paper, we propose to use multiple factors to measure and rank the relative generalization of DNNs based on a new concept of confidence dimension (CD). Furthermore, we provide a feasible framework in our CD to theoretically calculate the upper bound of generalization based on the conventional Vapnik-Chervonenk dimension (VC-dimension) and Hoeffding's inequality. Experimental results on image classification and object detection demonstrate that our CD can reflect the relative generalization ability for different DNNs. In addition to full-precision DNNs, we also analyze the generalization ability of binary neural networks (BNNs), whose generalization ability remains an unsolved problem. Our CD yields a consistent and reliable measure and ranking for both full-precision DNNs and BNNs on all the tasks.  ( 2 min )

We introduce the model selection problem in pure exploration linear bandits, where the learner needs to adapt to the instance-dependent complexity measure of the smallest hypothesis class containing the true model. We design algorithms in both fixed confidence and fixed budget settings with near instance optimal guarantees. The core of our algorithms is a new optimization problem based on experimental design that leverages the geometry of the action set to identify a near-optimal hypothesis class. Our fixed budget algorithm is developed based on a novel selection-validation procedure, which provides a new way to study the understudied fixed budget setting (even without the added challenge of model selection). We adapt our algorithms, in both fixed confidence and fixed budget settings, to problems with model misspecification.  ( 2 min )

We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space and carefully deal with the induced model misspecification. Our approach is conceptually very different from existing works that can either only handle low-dimensional linear bandits or passively deal with model misspecification. We showcase the application of our approach to two pure exploration settings that were previously under-studied: (1) the reward function belongs to a possibly infinite-dimensional Reproducing Kernel Hilbert Space, and (2) the reward function is nonlinear and can be approximated by neural networks. Our main results provide sample complexity guarantees that only depend on the effective dimension of the feature spaces in the kernel or neural representations. Extensive experiments conducted on both synthetic and real-world datasets demonstrate the efficacy of our methods.  ( 2 min )

Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may manifest heterogeneous structures and data quality as often existing data may be collected from different subjects under different conditions for each type of omics data. We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across such heterogeneous views, using a fused Gromov-Wasserstein (FGW) regularization between latent representations of corresponding views for integrative analysis. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, either with graph-structured or unstructured data in different views, but also increases the model flexibility with the distribution-based regularization. This allows efficient alignment of heterogeneous latent variable distributions to derive reliable interaction predictions compared to the existing point-based graph embedding methods. Our experiments on several real-world datasets demonstrate enhanced performance of MoReL in inferring meaningful interactions compared to existing baselines.  ( 2 min )

Automated decision support systems that are able to infer second opinions from experts can potentially facilitate a more efficient allocation of resources; they can help decide when and from whom to seek a second opinion. In this paper, we look at the design of this type of support systems from the perspective of counterfactual inference. We focus on a multiclass classification setting and first show that, if experts make predictions on their own, the underlying causal mechanism generating their predictions needs to satisfy a desirable set invariant property. Further, we show that, for any causal mechanism satisfying this property, there exists an equivalent mechanism where the predictions by each expert are generated by independent sub-mechanisms governed by a common noise. This motivates the design of a set invariant Gumbel-Max structural causal model where the structure of the noise governing the sub-mechanisms underpinning the model depends on an intuitive notion of similarity between experts which can be estimated from data. Experiments on both synthetic and real data show that our model can be used to infer second opinions more accurately than its non-causal counterpart.  ( 2 min )

While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost. The sliced Wasserstein distance and its variants improve the computational efficiency through the random projection, yet they suffer from low accuracy if the number of projections is not sufficiently large, because the majority of projections result in trivially small values. In this work, we propose a new family of distance metrics, called augmented sliced Wasserstein distances (ASWDs), constructed by first mapping samples to higher-dimensional hypersurfaces parameterized by neural networks. It is derived from a key observation that (random) linear projections of samples residing on these hypersurfaces would translate to much more flexible nonlinear projections in the original sample space, so they can capture complex structures of the data distribution. We show that the hypersurfaces can be optimized by gradient ascent efficiently. We provide the condition under which the ASWD is a valid metric and show that this can be obtained by an injective neural network architecture. Numerical results demonstrate that the ASWD significantly outperforms other Wasserstein variants for both synthetic and real-world problems.  ( 2 min )

Model-agnostic meta-learning (MAML) and its variants have become popular approaches for few-shot learning. However, due to the non-convexity of deep neural nets (DNNs) and the bi-level formulation of MAML, the theoretical properties of MAML with DNNs remain largely unknown. In this paper, we first prove that MAML with over-parameterized DNNs is guaranteed to converge to global optima at a linear rate. Our convergence analysis indicates that MAML with over-parameterized DNNs is equivalent to kernel regression with a novel class of kernels, which we name as Meta Neural Tangent Kernels (MetaNTK). Then, we propose MetaNTK-NAS, a new training-free neural architecture search (NAS) method for few-shot learning that uses MetaNTK to rank and select architectures. Empirically, we compare our MetaNTK-NAS with previous NAS methods on two popular few-shot learning benchmarks, miniImageNet, and tieredImageNet. We show that the performance of MetaNTK-NAS is comparable or better than the state-of-the-art NAS method designed for few-shot learning while enjoying more than 100x speedup. We believe the efficiency of MetaNTK-NAS makes itself more practical for many real-world tasks.  ( 2 min )

We currently witness the spectacular success of artificial intelligence in both science and public life. However, the development of a rigorous mathematical foundation is still at an early stage. In this survey article, which is based on an invited lecture at the International Congress of Mathematicians 2022, we will in particular focus on the current "workhorse" of artificial intelligence, namely deep neural networks. We will present the main theoretical directions along with several exemplary results and discuss key open problems.  ( 2 min )

Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. However, in general clustering tasks, the graph structure of data does not exist such that the strategy to construct a graph is crucial for performance. Therefore, how to extend graph convolution networks into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-Euclidean structure sufficiently. We further design a novel mechanism with rigorous analysis to avoid the collapse caused by the adaptive construction. Via combining the generative model for network embedding and graph-based clustering, a graph auto-encoder with a novel decoder is developed such that it performs well in weighted graph used scenarios. Extensive experiments prove the superiority of our model.  ( 2 min )

Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may enable such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Machine Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and synthesize new designs. Recently, DGMs such as feedforward Neural Networks (NNs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in engineering design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances to benefit researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported the development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion, we identify possible solution pathways as key areas on which to target future work.  ( 2 min )

Gaussian process regression underpins countless academic and industrial applications of machine learning and statistics, with maximum likelihood estimation routinely used to select appropriate parameters for the covariance kernel. However, it remains an open problem to establish the circumstances in which maximum likelihood estimation is well-posed. That is, when the predictions of the regression model are continuous (or insensitive to small perturbations) in the training data. This article presents a rigorous proof that the maximum likelihood estimator fails to be well-posed in Hellinger distance in a scenario where the data are noiseless. The failure case occurs for any Gaussian process with a stationary covariance function whose lengthscale parameter is estimated using maximum likelihood. Although the failure of maximum likelihood estimation is informally well-known, these theoretical results appear to be the first of their kind, and suggest that well-posedness may need to be assessed post-hoc, on a case-by-case basis, when maximum likelihood estimation is used to train a Gaussian process model.  ( 2 min )

There are many recent research efforts to fine-tune a pre-trained generator with a few target images to generate images of a novel domain. Unfortunately, these methods often suffer from overfitting or under-fitting when fine-tuned with a single target image. To address this, here we present a novel single-shot GAN adaptation method through unified CLIP space manipulations. Specifically, our model employs a two-step training strategy: reference image search in the source generator using a CLIP-guided latent optimization, followed by generator fine-tuning with a novel loss function that imposes CLIP space consistency between the source and adapted generators. To further improve the adapted model to produce spatially consistent samples with respect to the source generator, we also propose contrastive regularization for patchwise relationships in the CLIP space. Experimental results show that our model generates diverse outputs with the target texture and outperforms the baseline models both qualitatively and quantitatively. Furthermore, we show that our CLIP space manipulation strategy allows more effective attribute editing.  ( 2 min )

The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing high quality representations could be of great value. It is key to enabling transfer learning, which is especially beneficial for medical applications, where there is an abundance of data but labeling is costly and time consuming. We propose an unsupervised contrastive learning framework that is motivated from the perspective of label smoothing. The proposed approach uses a novel contrastive loss that naturally exploits a data augmentation scheme in which new samples are generated by mixing two data samples with a mixing component. The task in the proposed framework is to predict the mixing component, which is utilized as soft targets in the loss function. Experiments demonstrate the framework's superior performance compared to other representation learning approaches on both univariate and multivariate time series and illustrate its benefits for transfer learning for clinical time series.  ( 2 min )

As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models are limited in their ability to explain dynamic state changes in the brain which occurs spontaneously as a result of normal brain function. Hidden Markov Models (HMMs) trained on neuroimaging time series data have since arisen as a method to produce dynamical models that are easy to train but can be difficult to fully parametrise or analyse. We propose an interpretation of these neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models (HMGMs). This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. Furthermore, we propose a general method for selecting HMM hyperparameters in the absence of external data, based on the principle of maximum entropy, and use this to select the number of layers in the multiplex model. We produce a new tool for determining important communities of brain regions using a spatiotemporal random walk-based procedure that takes advantage of the underlying Markov structure of the model. Our analysis of real multi-subject fMRI data provides new results that corroborate the modular processing hypothesis of the brain at rest as well as contributing new evidence of functional overlap between and within dynamic brain state communities. Our analysis pipeline provides a way to characterise dynamic network activity of the brain under novel behaviours or conditions.  ( 3 min )

This paper proposes an algorithm (RMDA) for training neural networks (NNs) with a regularization term for promoting desired structures. RMDA does not incur computation additional to proximal SGD with momentum, and achieves variance reduction without requiring the objective function to be of the finite-sum form. Through the tool of manifold identification from nonlinear optimization, we prove that after a finite number of iterations, all iterates of RMDA possess a desired structure identical to that induced by the regularizer at the stationary point of asymptotic convergence, even in the presence of engineering tricks like data augmentation and dropout that complicate the training process. Experiments on training NNs with structured sparsity confirm that variance reduction is necessary for such an identification, and show that RMDA thus significantly outperforms existing methods for this task. For unstructured sparsity, RMDA also outperforms a state-of-the-art pruning method, validating the benefits of training structured NNs through regularization.  ( 2 min )

Random forests are a popular class of algorithms used for regression and classification. The original algorithm introduced by Breiman in 2001 and many of its variants are ensembles of randomized decision trees built from axis-aligned partitions of the feature space. One such variant, called Mondrian forests, was proposed to handle the online setting and is the first class of random forests for which minimax rates were obtained in arbitrary dimension. However, the restriction to axis-aligned splits fails to capture dependencies between features, and random forests that use oblique splits have shown improved empirical performance for many tasks. In this work, we show that a large class of random forests with general split directions also achieve minimax rates in arbitrary dimension. This class includes STIT forests, a generalization of Mondrian forests to arbitrary split directions, as well as random forests derived from Poisson hyperplane tessellations. Crucially, our rates adapt to sparsity in the sense that they depend only on the dimension of the relevant feature subspace as opposed to the ambient dimension of the feature space. This generalizes the known rates for Mondrian random forests. These are the first results showing that random forest variants with oblique splits can achieve minimax rates in arbitrary dimension. Our proof technique relies on the novel application of the theory of stationary random tessellations in stochastic geometry to statistical learning theory.  ( 2 min )

Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is utilized in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each direction's weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, $k$-means clustering, and $k$-nearest neighbor classification. When the distance is estimated from an unlabeled dataset, we establish the upper bound on distance estimation's accuracy and the number of samples sufficient for downstream task improvement. Finally, numerical experiments are presented to support the theoretical results in the paper.  ( 2 min )

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated into any type of GNN and is strictly more expressive (in terms the Weisfeiler--Lehman graph isomorphism test) than message-passing GNNs. Augmenting GNNs with TOGL leads to improved predictive performance for graph and node classification tasks, both on synthetic data sets, which can be classified by humans using their topology but not by ordinary GNNs, and on real-world data.  ( 2 min )

Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to future outcomes. In this paper, we consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to an extreme delay of reward signals, in which the agent can only obtain one reward signal at the end of each trajectory. A popular paradigm for this problem setting is learning with a designed auxiliary dense reward function, namely proxy reward, instead of sparse environmental signals. Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning. We establish a surrogate problem by Monte-Carlo sampling that scales up least-squares-based reward redistribution to long-horizon problems. We analyze our surrogate loss function by connection with existing methods in the literature, which illustrates the algorithmic properties of our approach. In experiments, we extensively evaluate our proposed method on a variety of benchmark tasks with episodic rewards and demonstrate substantial improvement over baseline algorithms.  ( 2 min )

Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model to be served to all clients, but this approach might be sub-optimal when clients' local data distributions are heterogeneous. In order to tackle this limitation, recent personalized federated learning methods train a separate model for each client while still leveraging the knowledge available at other clients. In this work, we exploit the ability of deep neural networks to extract high quality vectorial representations (embeddings) from non-tabular data, e.g., images and text, to propose a personalization mechanism based on local memorization. Personalization is obtained interpolating a pre-trained global model with a $k$-nearest neighbors (kNN) model based on the shared representation provided by the global model. We provide generalization bounds for the proposed approach and we show on a suite of federated datasets that this approach achieves significantly higher accuracy and fairness than state-of-the-art methods.  ( 2 min )

World Input-Output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (part of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and Matrix Completion (MC) with a LASSO-like nuclear norm penalty, to impute missing entries of a partially unknown I/O matrix. Through simulations based on synthetic matrices we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input-Output Database (WIOD) tables - which are an example of industry-by-industry I/O tables - is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them.  ( 2 min )

For marketing, we sometimes need to recommend content for multiple pages in sequence. Different from general sequential decision making process, the use cases have a simpler flow where customers per seeing recommended content on each page can only return feedback as moving forward in the process or dropping from it until a termination state. We refer to this type of problems as sequential decision making in linear--flow. We propose to formulate the problem as an MDP with Bandits where Bandits are employed to model the transition probability matrix. At recommendation time, we use Thompson sampling (TS) to sample the transition probabilities and allocate the best series of actions with analytical solution through exact dynamic programming. The way that we formulate the problem allows us to leverage TS's efficiency in balancing exploration and exploitation and Bandit's convenience in modeling actions' incompatibility. In the simulation study, we observe the proposed MDP with Bandits algorithm outperforms Q-learning with $\epsilon$-greedy and decreasing $\epsilon$, independent Bandits, and interaction Bandits. We also find the proposed algorithm's performance is the most robust to changes in the across-page interdependence strength.  ( 2 min )

Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries.  ( 2 min )

Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain. We address these issues for a particularly pervasive type of domain shift called measurement shift which can be resolved by restoring the source features rather than extracting new ones. In particular, we propose Feature Restoration (FR) wherein we: (i) store a lightweight and flexible approximation of the feature distribution under the source data; and (ii) adapt the feature-extractor such that the approximate feature distribution under the target data realigns with that saved on the source. We additionally propose a bottom-up training scheme which boosts performance, which we call Bottom-Up Feature Restoration (BUFR). On real and synthetic data, we demonstrate that BUFR outperforms existing SFDA methods in terms of accuracy, calibration, and data efficiency, while being less reliant on the performance of the source model in the target domain.  ( 2 min )

We study the performance of different pool-based Batch Mode Deep Active Learning (BMDAL) methods for regression on tabular data, focusing on methods that do not require to modify the network architecture and training. Our contributions are three-fold: First, we present a framework for constructing BMDAL methods out of kernels, kernel transformations and selection methods, showing that many of the most popular BMDAL methods fit into our framework. Second, we propose new components, leading to a new BMDAL method. Third, we introduce an open-source benchmark with 15 large tabular data sets, which we use to compare different BMDAL methods. Our benchmark results show that a combination of our novel components yields new state-of-the-art results in terms of RMSE and is computationally efficient. We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.  ( 2 min )

Biological intelligence is remarkable in its ability to produce complex behaviour in many diverse situations through data efficient, generalisable and transferable skill acquisition. It is believed that learning "good" sensory representations is important for enabling this, however there is little agreement as to what a good representation should look like. In this review article we are going to argue that symmetry transformations are a fundamental principle that can guide our search for what makes a good representation. The idea that there exist transformations (symmetries) that affect some aspects of the system but not others, and their relationship to conserved quantities has become central in modern physics, resulting in a more unified theoretical framework and even ability to predict the existence of new particles. Recently, symmetries have started to gain prominence in machine learning too, resulting in more data efficient and generalisable algorithms that can mimic some of the complex behaviours produced by biological intelligence. Finally, first demonstrations of the importance of symmetry transformations for representation learning in the brain are starting to arise in neuroscience. Taken together, the overwhelming positive effect that symmetries bring to these disciplines suggest that they may be an important general framework that determines the structure of the universe, constrains the nature of natural tasks and consequently shapes both biological and artificial intelligence.  ( 2 min )

Inspired by the numerical solution of ordinary differential equations, in this paper we propose a novel Reservoir Computing (RC) model, called the Euler State Network (EuSN). The introduced approach makes use of forward Euler discretization and antisymmetric recurrent matrices to design reservoir dynamics that are both stable and non-dissipative by construction. Our mathematical analysis shows that the resulting model is biased towards unitary effective spectral radius and zero local Lyapunov exponents, intrinsically operating at the edge of stability. Experiments on synthetic tasks indicate the marked superiority of the proposed approach, compared to standard RC models, in tasks requiring long-term memorization skills. Furthermore, results on real-world time series classification benchmarks point out that EuSN is capable of matching (or even surpassing) the level of accuracy of trainable Recurrent Neural Networks, while allowing up to 100-fold savings in computation time and energy consumption.  ( 2 min )

Exogenous heterogeneity, for example, in the form of instrumental variables can help us learn a system's underlying causal structure and predict the outcome of unseen intervention experiments. In this paper, we consider linear models in which the causal effect from covariates $X$ on a response $Y$ is sparse. We prove that the causal coefficient becomes identifiable under weak conditions and may even be identified in models, where the number of instruments is as small as the number of causal parents. We also develop graphical criteria under which the identifiability holds with probability one if the edge coefficients are sampled randomly from a distribution that is absolutely continuous with respect to Lebesgue measure. As an estimator, we propose spaceIV and prove that it consistently estimates the causal effect if the model is identifiable and evaluate its performance on simulated data.  ( 2 min )

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The three essential components that determine the evolution of modern Machine Learning are computing, data, and algorithmic advancements (ML). The article looks at trends in the most easily quantifiable element. Before 2010, training computes expanded in lockstep with Moore’s law, doubling every two years. Since the early 2010s, when Deep Learning was first introduced, the rate of training compute has quickened, roughly doubling every six months. Late in 2015, a new trend emerged. The history of computation in ML has been divided into three eras based on these observations – the Pre-Deep Learning Era, the Deep Learning Era, and the Large-Scale Era. The article summarises the fast-growing compute requirements for training advanced ML systems. Trends The comparison is made on a dataset of 123 milestone ML systems, annotated with the computing it took to train them. Before Deep Learning took off, there was a period of slow progress. The tendency accelerated in 2010 and hasn’t slowed since. Separately, in 2015 and 2016, a new trend of large-scale models arose, expanding at a comparable rate but by two orders of magnitude faster than the preceding one. Continue Reading The Research Summary Paper: https://arxiv.org/pdf/2202.05924.pdf Github: https://github.com/ML-Progress/Compute-Trends submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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In recent years, transformer topologies have advanced the state-of-the-art in a wide range of natural language processing (NLP) activities. Vision transformers (ViT) are now being used more frequently in computer vision. However, because of the quadratic complexity of transformers over input length, they consume a lot of energy, which limits their research and development and industrial deployment. An MLP-based Green AI Replacement to Transformers presents a novel Multi-Layer Perceptron (MLP) model, HyperMixer, as an energy-efficient alternative to transformers that preserves similar inductive biases, according to a recent article published by the Idiap Research Institute in Switzerland. The researchers demonstrate that HyperMixer may achieve performance comparable to transformers while significantly reducing processing time, training data, and hyperparameter tuning expenses. HyperMixer is a new all-MLP model with inductive biases inspired by transformers. On the GLUE benchmark, HyperMixer’s performance was compared to competitors’. HyperMixer learns attention patterns similarly to transformers, as demonstrated by this ablation. Continue Reading The Research Summary Paper: https://arxiv.org/pdf/2203.03691.pdf https://preview.redd.it/4dbukkw5d6o81.png?width=1502&format=png&auto=webp&s=52f61392525435cf105d4bfee508a4c05eef5389 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Hello, I'm working on my graduate paper regarding artificial intelligence being utilized for offensive based cyber attacks. Not just focusing on Adversarial AI, rather exploring AI use cases for malicious code generation or automated vulnerability discovery and exploitation. Would any SMEs within the community be interested in participating in a Chatham house rules interview regarding this topic? submitted by /u/Vicarfort [link] [comments]  ( 1 min )

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A project supported by the STFC Hartree Centre Discovery Accelerator accurately predicts patient response to treatments for ulcerative colitis and Crohn’s disease. Artificial intelligence may soon assist more than 6 million1 individuals worldwide who suffer from inflammatory bowel disease (IBD) in selecting the optimum medication for their illness. An explainable AI pharmacogenomics methodology we created effectively predicted how patients will respond — favorably or negatively — to an IBD treatment 95% of the time, according to research published in PLOSone. Chronic inflammatory bowel diseases (IBDs) such as ulcerative colitis and Crohn’s disease are caused by clinical, genetic, and environmental variables such as nutrition and lifestyle. Even though all patients have the same symptoms, there is no one-size-fits-all treatment for IBD that is helpful for everybody. Choosing the optimum therapy for a patient is still a trial-and-error procedure for both the doctor and the patient. According to researchers at IBM Research in the UK and REPROCELL, a stem cell and fresh tissue research firm, used IBD patient data and explainable AI approaches to study treatment reactions with the help of the STFC Hartree Centre’s Discovery Accelerator. Their objective was to discover the optimum medications for IBD therapies less of a guessing game. The resulting collection of algorithms demonstrated that it was feasible to crack the IBD data black box and comprehend forecast and explain how persons with IBD could react to different medications on the market and under development. Continue Reading Our Research Summary Paper: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263248 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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Our lab is recently doing more deep learning projects and our current lab server is struggling with the increasing load. My professor tasked me with finding our lab a good GPU server for the lab. The reserved budget is 25K. Our current server have the following specifications: CPU: Intel Xeon Silver 4114 CPU RAM: 192GB GPU: NVIDIA Tesla V100 PCIe 32GB (one GPU) There are 4 phd students currently working on the server. I prefer something better than the current server. So far I found Lambda Labs and ThinkMate (based on a friend recommendation). My professor want me to select a vendor with good support and offering academic discounts. I'm looking for GPUs that are perform similar or better than Nvidia RTX 2080 Ti. Do you have a recommendation for any vendor that builds GPU servers (~$25K, servicing 3-5 students working mostly on CNN and RNN models), and which server configurations is best? Thank you. submitted by /u/majax21 [link] [comments]  ( 3 min )

Hi everybody, I developed a new NN training approach and want to benchmark it now. I am looking for Classification/Regression tasks on various normal and image datasets, but also autoencoder-like reconstruction problems (I guess denoising is here the classic evaluation task?) Are there any good websites/papers which collect current SOTA results on multiple datasets? It would be great if they also provide training times, number of parameters, etc. What datasets would you recommend to definitely include in such a benchmark? Thanks in advance! submitted by /u/arcxtriy [link] [comments]  ( 1 min )

Hi everyone, I have a dataset of continuous multivariate samples. I'm clustering these samples together using an unsupervised algorithm to infer unmeasured latent states. Ideally, this would just be performed on the entire sample set. However, this label will then subsequently form part of a supervised prediction algorithm, and because the dataset isn't very large (several hundred samples), I'm almost certainly going to end up using k-fold cross validation. ​ Now, the issue with using cluster labels generated from the whole dataset is that this essentially permits data leakage from the test fold back into the training fold for the supervised learning algorithm (which, for the purposes of my experiment, I don't want). So instead, the unsupervised labels will need to be generated for each fold individually. ​ What I would like to do is have a measure of the relative stability/instability of label assignment for each sample across the folds. So for example, over 10 folds, ideally sample 1 would always get the label 'A' ten times out of ten... but in reality, perhaps it will get label 'A' eight times, and label 'B' twice. ​ It sounds like the Bhattacharyya coefficient might be an appropriate scoring system (i.e. for each sample, calculate the square root of the product of the label proportions, and sum over all samples) - but I've never used this before, so I don't know whether it would be an appropriate use case. Does anyone have any familiarity with either this measure or this type of situation in general? submitted by /u/mcflyanddie [link] [comments]  ( 1 min )

Hi all, I've been thinking about how to democratize running and training of large language models, and more generally how to democratize all large models. I've wondered if this would be achievable with a volunteer computing project. I've found two (and only two, unfortunately) papers on the subject, so I at least know that I'm not the only one thinking about this. Those papers can be found here and here. That would be the end of the discussion for me, but I think the previous approaches have some flaws. Both papers use data parallelism, making training of extremely large (> 20GB) models basically impossible, because very few volunteers will have enough resources to run, much less train, the models, regardless of batch size. Systems like Hydra might be able to run things locally for models between 20 to 250GB, but above that it becomes a question of disk space. A more theoretical flaw is that both of the papers use a modified version of SGD, which means any model would be required to use special optimizers. I don't know how that would impact results/convergence, but it doesn't seem trivial. In conclusion, it seems to me that a VC system for extremely large models probably needs a novel approach that uses tensor parallelism, so that work could be more granularly distributed. It's (probably) not an easy problem, but it seems possible. I'm curious what this community's thoughts are, and if anyone would also be interested in working on this. Also, if anyone knows any other resources on this problem, please drop a link so I can check them out! submitted by /u/Lazauya [link] [comments]  ( 1 min )

In the 2 links below, it says you can commit to conferences such as ACL 2022 and NAACL 2022 at the same time submit to ARR for resubmission, but it doesn't say if you can do the same for workshops or conferences relating to ARR other than the 2 conferences listed above. In the last link, it pretty much specifies two conferences (maybe just listed as an example?) that you can commit and at the same time submit to ARR for resubmission, but for some reason doesn't directly say that you can commit to ARR related conference/workshop alongside submitting to ARR for resubmission. So if anybody has any info on whether I can submit to ARR for resubmission and at the same time commit to any ARR related workshop/conference, I'd appreciate it. https://www.2022.aclweb.org/post/acl-2022-chair-blog-post-faq https://aclrollingreview.org/choices/ submitted by /u/SiegeMemeLord [link] [comments]  ( 1 min )

I was playing with the idea of trying a transformer for a sequence modelling task for the first time recently - I was interested in all the X-former variants interested in better training stability, eg. Catformer or rezero or adding gates to the residual connections. I found I always got late NaN losses whenever the transformer blocks contained layer normalization. Removing layer normalization or using catformer (which has no layer normalization) this didn't happen. I was curious about if anyone knows why this is the case, and what current ideas are about the necessity of layer normalization to the transformer. submitted by /u/WigglyHypersurface [link] [comments]  ( 1 min )

I’m running train tests on data that is quite large (1.5M, 400). I need to preprocess such data with a data transformation and model-based data imputation pipeline. Then I need to run train/test fits on a neural net that needs PyTorch and GPU. With this in mind, what is the most cost-effective aws ec2 instance for large data processing that has gpu as well? Right now I’m either running it all on a p3.2xlarge, or I use a c5d.9xlarge for the data preprocessing, save that file and then move back to p3.2xlarge to run the train/test. Second option is not ideal because I’d want to run several tests quickly even when changing around feature engineering or features selected. submitted by /u/Gioamorim80 [link] [comments]  ( 1 min )

Hello, we are publishing our first paper as undergraduate students today. It is achieving SOTA on the BDD100K dataset (2 out of 3 tasks, at least). ​ Paper: https://arxiv.org/abs/2203.09035 Code: https://github.com/datvuthanh/HybridNets ​ Network architecture: HybridNets architecture ​ Contributions: HybridNets, an end-to-end perception network, achieving outstanding results in real-time on the BDD100K dataset for 3 tasks: traffic object detection, drivable area segmentation (not SOTA), and lane line detection. Automatically customized anchor for each level in the weighted bidirectional feature network, on any dataset. An efficient training loss function and training strategy to balance and optimize multi-task networks. submitted by /u/xoiga123 [link] [comments]  ( 1 min )

(First author here.) Are you interested in improving the sample-efficiency of neural network regression through active learning? Then, our new paper might be all you need 🙂 Paper: https://arxiv.org/abs/2203.09410 Code: https://github.com/dholzmueller/bmdal_reg Short summary: Using random projections of NN gradients as features and running a simple clustering method on these works really well, is scalable and convenient to use through our code. Long summary: We study pool-based batch mode deep active learning (BMDAL) for regression: We start with a small labeled training data set and repeatedly select a batch of unlabeled data from a large pool set for labeling. We want to select large batches since NN training can be slow. ​ Deep Batch Active Learning loop We propose a benchmark wi…  ( 3 min )

I have started my PhD in application of machine learning in health care. I want to understand what it takes to publish in papers like Journal of the American Medical Informatics Association (JAMIA) . Are there any patterns in the publication that one can look out for? Do they always need the best state of the art results or a novel algorithm? submitted by /u/Complex_State9960 [link] [comments]  ( 2 min )

I would like to discuss what people their experiences are with labeling tools for action localization in videos. Currently I only found CVAT to be somewhat decent (but still pretty bad). Besides this there are some Github repos that publish a (not/semi) working solution, but they also don't do the trick for me. My biggest issue with CVAT is that I can only upload 1 video per task and that it is limited to 500MB. I would like to know what other tools people have used and what your experiences are? submitted by /u/FreddyShrimp [link] [comments]  ( 1 min )

https://youtu.be/O_dJ31T01i8 Catastrophic forgetting is a big problem in mutli-task and continual learning. Gradients of different objectives tend to conflict, and new tasks tend to override past knowledge. In biological neural networks, each neuron carries a complex network of dendrites that mitigate such forgetting by recognizing the context of an input signal. This paper introduces Active Dendrites, which carries over the principle of context-sensitive gating by dendrites into the deep learning world. Various experiments show the benefit in combatting catastrophic forgetting, while preserving sparsity and limited parameter counts. ​ OUTLINE: 0:00 - Introduction 1:20 - Paper Overview 3:15 - Catastrophic forgetting in continuous and multi-task learning 9:30 - Dendrites in biological neurons 16:55 - Sparse representations in biology 18:35 - Active dendrites in deep learning 34:15 - Experiments on multi-task learning 39:00 - Experiments in continual learning and adaptive prototyping 49:20 - Analyzing the inner workings of the algorithm 53:30 - Is this the same as just training a larger network? 59:15 - How does this relate to attention mechanisms? 1:02:55 - Final thoughts and comments ​ Paper: https://arxiv.org/abs/2201.00042 Blog: https://numenta.com/blog/2021/11/08/can-active-dendrites-mitigate-catastrophic-forgetting submitted by /u/ykilcher [link] [comments]  ( 1 min )

I'm curious what people think of Apple's new Mac Studio as a potential workstation for machine learning and data processing tasks? It seems like Tensorflow can makes use of the M1 chip, but not Pytorch yet - I don't know how these compare to traditional GPU setups though? More generally, do you think the Mac Studio would be a good dev machine and good for other data-based work? I'm not sure, as it seems like the Studio is being pitched for people doing things like video editing rather than coding. Thanks! submitted by /u/uio8 [link] [comments]  ( 2 min )

You are welcome to apply for the Nordic Probabilistic AI School (ProbAI) 2022 being held on June 13-17 in Helsinki (Finland). APPLY NOW — The application deadline is March 27. About ProbAI 2022 The mission of the third Nordic Probabilistic AI School (ProbAI) is to provide an inclusive education environment serving state-of-the-art expertise in machine learning and artificial intelligence. The public, students, academia and industry are welcome to join ProbAI 2022. ProbAI is an intermediate to advanced level "summer" school with the focus on probabilistic machine learning. Covered are topics such as probabilistic models, variational approximations, deep generative models, latent variable models, normalizing flows, neural ODEs, probabilistic programming, and much more. The ProbAI 2022 w…  ( 3 min )

Those of you who did/doing a PhD recently, how did you decide your PhD topic? My advisor seems to give me a lot of freedom, by letting me choose my own topic, but has said if it is away from his interests he can't help a lot. He also would apply for funding, once I have a clear topic, which adds even more pressure. He wants me to write a survey paper, so that both of us can know the literature in the field. I've tried looking at the research by AI companies, but most of the areas are completely crowded. Which brings me to my question, how do you go about choosing your topic? Was a broad idea given to you by your advisor? Were you also expected to know everything about your field? How did you have the confidence in selecting a topic, given most of recent papers is just randomly adding/shifting/removing layers (no offense). Also, what did you expect/get from your advisor? Given my situation, it seems like I'll have to run the show, which makes me anxious. submitted by /u/Bibbidi_Babbidi_Boo [link] [comments]  ( 7 min )

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Conversational AI can deliver powerful, automated, interactive experiences through voice and text. Amazon Lex is a service that combines automatic speech recognition and natural language understanding technologies, so you can build these sophisticated conversational experiences. A common application of conversational AI is found in contact centers: self-service virtual agents. We’re excited to announce that you […]  ( 9 min )

AI isn't known for being able to solve the big problems, but what about the VERY large problems, such as possible futures to strive for? I decided to find out if I could get GPT-3 to come up with new ideas for utopias. Since GPT-3 works by predicting  ( 3 min )

AI Weirdness: the strange side of machine learning  ( 1 min )

Examining a module consisting of several attention layers running in parallel.  ( 4 min )

NVIDIA’s GTC conference is packed with smart people and programming. The virtual gathering — which takes place from March 21-24 — sits at the intersection of some of the fastest-moving technologies of our time. It features a lineup of speakers from every corner of industry, academia and research who are ready to paint a high-definition Read article > The post Hopped Up: NVIDIA CEO, AI Leaders to Discuss Next Wave of AI at GTC appeared first on NVIDIA Blog.  ( 3 min )

So cross-entropy(H(p,q)) and KL-divergence (KL(p||q)) relate to each other as follows: H(p,q) = KL(p||q) + H(p) and KL(p||q) = H(p,q) - H(p) where p is the data distribution and q is the model distribution. When p is constant (as is the case in most ML problems), minimizing H(p,q) is equivalent to minimizing KL(p||q). However, there seems to be some ambiguity about this. (One practitioner claims)[https://stats.stackexchange.com/a/409271] that there is a difference in practice, because during batch gradient descent the data distribution p' in each batch is noisy and harder learn for the model, leading to worse performance for the KL-divergence. I am skeptical about his claim, as H(p) is part of both cross-entropy and KL-divergence, depending on how one views them. If anything, the KL-divergence should work better because it does not directly incorporate H(p). What is your experience / your thoughts? submitted by /u/optimized-adam [link] [comments]  ( 1 min )

Hello to everyone, At the moment exist any way to avoid facial recognition from social media? Not Fawkes.... Another way because I think AI learned that the picture is modified with this software. It is possible that the software recognize this pictures as fake face? Also combine fawkes with another software as Photoshop could be a good idea to look as another user? In social media the people use Photoshop... I don't know if is still possible to recognize easy Thank you submitted by /u/Maleficent_Camel5718 [link] [comments]  ( 1 min )

Hi everyone. For a project i need to find a way to extract some "potential user profiles" from firewall logs, such as the pic i attach to this post. For example i would like to infer that user A belongs to the "administrative" part of the company whyle Bob belongs to the tecnician department. Any hints on how to do this? I taught about using k-means on some certain fields such as destination, source, protocol. The problem is that k-means flags different records of the same user with different clusters, and i guess this may not be a good strategy (I might try to use some kind of smoothening in which i flatten the cluster, for example if BoB has labels 0 0 0 0 0 1 1 1 1 0 0 0 0 0 then bob has cluster 0). ​ https://preview.redd.it/tehf5axof0o81.png?width=1489&format=png&auto=webp&s=720ab6f792e2d409735e662d602e76ac7909e23b submitted by /u/Set-New [link] [comments]  ( 1 min )

I am looking to adapt GPT-2 to generate dialogue utterances in the style of a certain demography/population (let's call them Ogres). However, there are no large datasets that are both 1) dialogue datasets, and 2) are generated by this target demography. In the absence of such data, I have been considering a few approaches for data augmentation purposes. Many of those approaches would benefit from a GPT-Ogre, which is at least capable of generating text similar to Ogres, if not necessarily dialogic. Approach 1 ========== For this, I am considering performing additional pre-training of, say, GPT-2 on some medium-sized corpora generated by Ogres. This sounds like something that should have been done by a lot of people for a lot of different things by now, but except for some papers that have tried to do this with BERT in the Medical domain, I was not able to find any papers/GitHub repos that have done this with additional unsupervised pre-training GPT. It would be helpful if someone could point me to some resources around this as I feel the space of hyperparameters to figure out the best learning rate, etc. is too large, and if somebody has already done this, it would be easy to replicate it. Approach 2 ========== There are some dialogue-specific GPT models such as DialoGPT that have been fine-tuned (in a supervised way; mind you, not pretrained in an unsupervised way). However, it is not in the Ogre style. I am wondering if it's a ridiculous idea to perform additional pre-training of a fine-tuned GPT-2 model? submitted by /u/exceptaway343 [link] [comments]  ( 1 min )

Ancient history relies on disciplines such as epigraphy — the study of inscribed texts known as inscriptions — for evidence of the thought, language, society and history of past civilizations. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. This work presents Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Paper: https://www.nature.com/articles/s41586-022-04448-z Code: https://github.com/deepmind/ithaca Online interface: https://ithaca.deepmind.com/ Blog: https://deepmind.com/blog/article/Predicting-the-past-with-Ithaca Video: https://www.youtube.com/watch?v=rq0Ex_qCKeQ submitted by /u/yannisassael [link] [comments]  ( 1 min )

Deepspeed? FSDP? FFCV? XYZK? what do they all mean and how can you use all of them to speed up your model training? All amazing techniques developed by world-class teams and are (or are being made) accessible via PyTorch Lightning! If you know of other techniques you want to be integrated, please comment below! https://william-falcon.medium.com/pytorch-lightning-vs-deepspeed-vs-fsdp-vs-ffcv-vs-e0d6b2a95719 submitted by /u/waf04 [link] [comments]  ( 1 min )

Hi reddit experts, I know that for any machine learning and AI application, you need data in order to train your model. I would like to ask the following: Where does this data usually come from? (For instance, is this data provided by the customer? Your professor? Is it always assumed that this data will be available? Can you trust the source?) If you don’t already have this data, will you be asked to implement a system to collect this data? Would the accuracy of this data be important to the model? Meaning, let’s say if we wanted to train the model of an AI temperature controller. Would it really matter if the data was just 25 degrees? Versus 25.58 degrees? Is it important that this data is sampled fast enough? Meaning, if I only had 1 data point per day, versus 10 data points per minute? Here’s some context to why I’m asking. I’ve been a measurement and instrumentation engineer for a good part of my career. We set up measurement systems that allow customers to collect all kinds of real-world data, including temp, vibration, voltage, images, etc. The other day I had a debate with a friend who does AI, where he claimed that “collecting the data" was a trivial effort, because most of time he just uses the data that’s been given to him. But I challenged him, because there's lots of know-how needed in acquiring real-world physical data (for ex: which sensors to use, how to avoid noise, how to synchronize timestamps, etc.) All of this know-how ensures that the collected data is accurate, clean, and complete. But he just kinda brushed this off, and said that most ML/AI algorithms nowadays just look at the general trend of data, absolute accuracy doesn’t really matter, but there still needs to be some relative variability, then he can work with it. I’m having a hard time believing this is true. Can anyone shed some light on this subject? submitted by /u/sharkera130 [link] [comments]  ( 2 min )

https://www.cs.unh.edu/~mpetrik/tutorials/risk/ submitted by /u/Ok_Can2425 [link] [comments]  ( 1 min )

Hello! just made my first gan and am keen to try make one in 1024x1024 I am looking to upgrade my gpu processing power by adding a RTX A4000 16g to my existing RTX 2080 8g will the memory stack as I have been working around some cuda memory allocation issues so far its working but Id like to know before I put down that kind of money will I be able to go past a 8g limit if I add the A4000 to my rig I have tried googling this question to no clear answer, feel free to link something related to this. submitted by /u/Trainsmurf [link] [comments]  ( 2 min )

I am obsessed with machine learning despite not being very smart at it myself. But specifically, I love it in games. I love interacting with it. I'm looking for more games like these: I play NovelAI, Creatures 3, Wobbledogs, AI Dungeon, Replika, and I was going to pick up Species: Artificial Life Real Evolution. Someone made a hook of what I think is GPT 2.7B to Crusader Kings so that you could talk to the different countries' leaders. Wobbledogs uses it for dog's walking. Motivation and desire are present. It also has positive / negative reinforcement. AI Dungeon, Replika, and NovelAI are all GPT - OpenAI GPT Da Vinci (for AID Dragon), OpenAI GPT 2.0 (of some sort, for Replika), and Fairseq / EleutherAI GPT models (for NovelAI). Creatures 3 was one of the first pseudo(?) neural network games and utilizes motivation, desire, and positive / negative reinforcement. Species: Artificial Life Real Evolution is an abandoned game which simulated lifeforms evolving in stressful situations. They don't seem to actually be neural network, but it's close enough to be very interesting - creatures have motivations and desires but do not experience positive / negative reinforcement. There's also 60,000 of them at once... I guess it's sort of also got generational adaptation! PS, if anyone wants to obsess over these awesome games with me, let's do it! submitted by /u/WiIdCherryPepsi [link] [comments]  ( 1 min )

Hi! We introduce our new paper "On Embeddings for Numerical Features in Tabular Deep Learning". Paper: https://arxiv.org/abs/2203.05556 Code: https://github.com/Yura52/tabular-dl-num-embeddings TL;DR: using embeddings for numerical features (i.e. using vector representations instead of scalar values) can lead to significant profit for tabular DL models. Let's consider the vanilla MLP taking two numerical inputs. https://preview.redd.it/yb55tdw27wn81.png?width=330&format=png&auto=webp&s=a6fc53e8611baee6993aab47480f0a6a6b85e46c Now, here is the same MLP, but now with embeddings for numerical features: https://preview.redd.it/zebl8tld7wn81.png?width=368&format=png&auto=webp&s=3d20652075d0543c7d6c70f34d67140bc2c6346b The main contributions: we show that using vector representations instead of scalar representations for numerical features can lead to significant profit for tabular DL models we show that MLP-like models equipped with embeddings can perform on par with Transformer-based models we make some progress in the "DL vs GBDT" competition submitted by /u/Yura52 [link] [comments]  ( 2 min )

FRUFS is a Feature Relevance based Unsupervised Feature Selection approach using supervised algorithms such as XGBoost, etc. This is a new algorithm that I developed and put down in the form of a blog. Here's the link to the article. While reading the blog you will also re-discover an influential research paper in this domain on your own! FRUFS was evaluated on 4 different datasets under both unsupervised and supervised settings. The results are available on the blog as well as in the table below. Dataset Task All Features FRUFS Metric MNIST Unsupervised 50.48 53.70 NMI Waveform Unsupervised 38.20 39.67 NMI Ionosphere Supervised 88.01 91.45 Accuracy Adult Supervised 62.16 62.65 F1-score ​ Fig. 1 shows the feature importance of FRUFS (left) in comparison to the actual feature importance (right) on the MNIST dataset. This was done without any labels. Fig. 1: Visualizing feature/pixel importance of MNIST dataset ​ I have also developed a scikit-learn compatible library in case you want to use FRUFS for your own project. Here's the github repo. A simple pip install FRUFS will do the trick! Do give the blog a read and let me know your thoughts. submitted by /u/atif_hassan [link] [comments]  ( 1 min )

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A promising family of generative models has emerged: score-based generative models (SGMs) and denoising diffusion probabilistic models. SGMs have applications in image, voice, and music synthesis, image editing, super-resolution, image-to-image translation, and 3D shape generation because they provide high-quality synthesis and sample variety without requiring adversarial aims. SGMs use a diffusion process to progressively introduce noise to the data, changing a complicated data distribution into a tractable prior distribution for analysis. The modified data’s score function—the gradient of the log probability density—is then learned using a neural network. To synthesize new samples, the learned scores can be used to solve a stochastic differential equation (SDE). Inverting the forward diffusion corresponds to an iterative denoising process. Continue Reading Paper: https://arxiv.org/pdf/2112.07068.pdf Project: https://nv-tlabs.github.io/CLD-SGM/ Code: https://github.com/nv-tlabs/CLD-SGM ​ https://i.redd.it/b2wxorotuzn81.gif submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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Hi all! Anybody interested in reading an AI-modified story? I created a user study where a short story of about 3000 words has been modified with NLP, and one of the various versions is shown to the reader. The system gives you a very short personality test, and asks you a few questions on what you thought of the story. There are Amazon vouchers worth 5 GBP available for those doing this now, in any country! People found not to have done the study carefully enough might be excluded. It's at https://cci.arts.ac.uk/~wnybom/cloak.html submitted by /u/wnybom [link] [comments]  ( 1 min )

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Hi, I recently enrolled for AI in a local college, I find AI as a whole fascinating. One problem that I've run into is, the concept of user agents. From my understanding, user agents are the entities that try to obtain a goal, such as a human playing chess, here the human would be the user agent. The humans sensors would be their eyes and brain. The humans actuators would be their hands. Similarly an autonomous taxi could be a user agent, the taxis sensors could be cameras, speedometer, etc. It's actuators could be steering, breaks, horn, signals etc. If you can tell by the latter example above, I'm currently reading AI: A modern approach(3rd edition). My question is; I've learned about some basic search algorithms such as DFS and BFS. I've implemented a DFS algorithm to solve a maze ie get to the exit node. What is my user agent in this program? Is the user agent the algorithm itself(DFS)?? Or do I have to create a class and put the algorithm as a function(I'm using Python). Thanks :) submitted by /u/Adam20188 [link] [comments]  ( 1 min )

Hello, Text processing AI has made great progress these last years but the main focus is on the English language (understandably). I think that many people are trying to do Natural Language Processing in non-English languages but are disappointed by the results. It is especially hard with text generation models like GPT-3, GPT-J, GPT-NeoX... In this article, I'm trying to quickly summarize what the options are today for people trying to use a multilingual AI: https://nlpcloud.io/multilingual-nlp-how-to-perform-nlp-in-non-english-languages.html If you can think of additional solutions not mentioned in this article please let me know! submitted by /u/juliensalinas [link] [comments]  ( 1 min )

Social engagement can be challenging for children who are born blind. Despite a great desire to do so, many blind children and young people with low vision fail to engage and befriend individuals in their age group. This can be extremely difficult for the kid or adolescent, as well as their support network of family members and instructors who wish to assist them in making these crucial connections. Microsoft team has recently developed “PeopleLens,” an open-ended AI system that provides additional resources to people who are blind or have low vision to make sense of and interact with their local social settings. The system uses Nreal Light augmented reality glasses connected to a smartphone, allowing them to expand their existing talents and abilities. Quick Read: https://www.marktechpost.com/2022/03/16/microsoft-introduces-peoplelens-an-open-ended-artificial-intelligence-system-that-uses-computer-vision-algorithms-to-help-young-people-who-are-blind-to-engage-with-their-immediate-social-surround/ Paper: https://www.microsoft.com/en-us/research/uploads/prod/2021/06/Morrison-Interactions2021\_PeopleLens.pdf Microsoft Blog: https://www.microsoft.com/en-us/research/blog/peoplelens-using-ai-to-support-social-interaction-between-children-who-are-blind-and-their-peers/ https://preview.redd.it/wvol1y7nnvn81.png?width=1542&format=png&auto=webp&s=d95e16d28797ce795634902cf02a125a05839312 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

I created a new project, and am currently looking for people willing to help: https://botbox.dev/ice-dragon-ai/ it's a free AI art generator If you are willing to help, either email me (you can find it in the link) or DM me on reddit submitted by /u/Recent_Coffee_2551 [link] [comments]

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A promising family of generative models has emerged: score-based generative models (SGMs) and denoising diffusion probabilistic models. SGMs have applications in image, voice, and music synthesis, image editing, super-resolution, image-to-image translation, and 3D shape generation because they provide high-quality synthesis and sample variety without requiring adversarial aims. SGMs use a diffusion process to progressively introduce noise to the data, changing a complicated data distribution into a tractable prior distribution for analysis. The modified data’s score function—the gradient of the log probability density—is then learned using a neural network. To synthesize new samples, the learned scores can be used to solve a stochastic differential equation (SDE). Inverting the forward diffusion corresponds to an iterative denoising process. Continue Reading Paper: https://arxiv.org/pdf/2112.07068.pdf Project: https://nv-tlabs.github.io/CLD-SGM/ Code: https://github.com/nv-tlabs/CLD-SGM ​ https://i.redd.it/8dl9ftuquzn81.gif submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Hi all, I apologize for the lack of technical knowledge here, as I have a biology background and am so very much out of my wheelhouse here. I was wondering if it was possible to just get a neural network that has already been trained to analyze keystroke biometric data and build profiles off of that information. We are having participants type out a sentence from Alice in Wonderland with a character limit of 100 characters. Background: I am a forensic science masters student with a biology background. My masters program has a cybercrime course where we have to conduct a research project with a digital forensic focus, and we've had very little guidance thus far. My project group decided to do a keystroke biometric comparison between English typing vs foreign language typing to see if both profiles could be tied back to one individual (eg, does switching between languages when typing affect typing manner and thus change the keystroke profile of one individual). Unfortunately, this has become a much more technical project than anticipated, and , as I mentioned, NONE of us has any real digital background. Our lecturer just sort of left us hanging and said "cool, go build and train your own neural network for this project", while my group has no idea HOW to actually do that. Thank you so much for any and all help! As I said, I deeply apologize for my lack of knowledge in this subject, and hope my post here is adequate. submitted by /u/determinedkorra [link] [comments]  ( 1 min )

Organizational forms serve as a primary business tool across industries—from financial services, to healthcare, and more. Consider, for example, tax filing forms in the tax management industry, where new forms come out each year with largely the same information. AWS customers across sectors need to process and store information in forms as part of their […]  ( 6 min )

Posted by Amir Yazdanbakhsh, Research Scientist and Aviral Kumar, Student Researcher, Google Research Advances in machine learning (ML) often come with advances in hardware and computing systems. For example, the growth of ML-based approaches in solving various problems in vision and language has led to the development of application-specific hardware accelerators (e.g., Google TPUs and Edge TPUs). While promising, standard procedures for designing accelerators customized towards a target application require manual effort to devise a reasonably accurate simulator of hardware, followed by performing many time-intensive simulations to optimize the desired objective (e.g., optimizing for low power usage or latency when running a particular application). This involves identifying the right ba…  ( 9 min )

CSAIL scientists came up with a learning pipeline for the four-legged robot that learns to run entirely by trial and error in simulation.  ( 5 min )

The AI-Guided Ultrasound Intervention Device is a lifesaving technology that helps a range of users deliver complex medical interventions at the point of injury.  ( 7 min )

Hi, I programmed a neural network "framework" in C++ which uses CUDA. It's my own implementation, and it's probably really slow, since I'm a beginner and don't even know how oop works (it's all one big file). The project is more about understanding the math behind it, but I still want to know the algorithm's performance in relation to some widely used frameworks like TensorFlow or Keras. It does manage to keep the GPU at 80 - 90% usage for a batch sizes of around 25-50 and 32-64 neurons per layer, if that says anything. However, is there something like "calculated derivatives per second", as a measurement of the speed of the implementation? That would be really nice to know. My implementation updates the network around 13 times per second with a batch size of 50 and a topology of { 6,32,32,6 }. How slow is that? I tried to train the network to calculate the acceleration present in a 3 body planet system. It struggles to do so, although the adam optimizer really helped. I think it got the first digit of the acceleration close to consistently right after one night of training, but it seemed, as if the learning speed was accelerating. The topology was { 6,64,64,6 }, batch size 50 and the learning rate 0.025. English is not my native language, so please forgive my mistakes. submitted by /u/qwedp [link] [comments]  ( 1 min )

Hi, I was going through the below question picked from reinforcement learning book (sutton & barto) ​ Consider an MDP with a single non terminal state and a single action that transitions back to the non-terminal state with probability p and transitions to the terminal state with probability 1-p. Let the reward be +1 on all transitions and let gamma = 1 (discount factor). Suppose you observe one episode that last for 10 steps, with a return of 10. What are the first-visit and every visit estimators of the value of the non terminal state. ​ Hi, I am not looking for the answer but the steps that take you to the answer. I am beginner in Reinforcement learning. Please help me. Thank you. submitted by /u/Tricky-Jello-7847 [link] [comments]  ( 1 min )

I was wondering if RecSim NG is actively used for research. Seems like there are limited resources and demos to play with. submitted by /u/Few-Cap-5989 [link] [comments]  ( 1 min )

Hi, I reimplemented Behavior From the Void: Unsupervised Active Pre-Training(official implementation https://github.com/rll-research/url_benchmark) Because, their APT only supports vector-based environment and ICM-based implementation(that is different version from APT paper). So I reimplemented it as paper described and replemented APT shows very interesting at first(showed significant behaviors and received 20~30 episode reward constantly and deceased apt encoder error) but at actual transfer step, It shows very bad performance. Q value looks overestimated when training Q network in pretraining step. Orange : scratch DrQ, gray : Finetuned version after APT 2M frames training ​ Orange : scratch DrQ, gray : Finetuned version after APT 2M frames training What should I do training APT correctly? My implementation is based on DrQ and APT official implementation https://github.com/seolhokim/APT Really Thanks for reading. submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 1 min )

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These are my favorite free Datacamp courses to learn in-demand data skills like Python, SQL, Power BI, Tableau, Seaborn, Matplotlib, Data…  ( 5 min )

It’s never been easier to be a PC gamer. GeForce NOW is your gateway into PC gaming. With the power of NVIDIA GeForce GPUs in the cloud, any gamer can stream titles from the top digital games stores — even on low-powered hardware. Evolve to the PC gaming ranks this GFN Thursday and get ready Read article > The post Everyone’s a PC Gamer This GFN Thursday appeared first on NVIDIA Blog.  ( 3 min )

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The NHS is now employing a cutting-edge AI program that can diagnose heart illness in just 20 SECONDS. Experts claim that the computer tool replicates human abilities but with more precision. When the patient is in the scanner, it analyses cardiac MRI images in about 20 seconds. This is far faster than a human doctor would take, which may take up to 13 minutes. It can also identify changes in the heart’s anatomy with a 40 percent higher accuracy. While the patient is in the scanner, the computer tool, which resembles human ability but with more precision and speed, can analyze cardiac MRI data in 20 seconds. Continue Reading The Research Summary https://preview.redd.it/rg4nmzlqptn81.jpg?width=6000&format=pjpg&auto=webp&s=c8f99ff5479cf9d5dfe6184073cc86664bbbba17 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

what are the best resources for studying LIME models in XAI? also what are the research gaps in this area? submitted by /u/Aromatic-Ad-2235 [link] [comments]

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Hello! This is my first post here so sorry if I don't phrase my topic of discussion well. For context, I'm doing a conceptual project about creating a more positive narrative on our future with AI focusing on one aspect of how it can benefit us in our daily lives. It will be an illustrated story to tell said narrative, not so much a research document (not smart enough for that). The question basically is, what are your thoughts about the different ways and areas AI could improve our lives in which it hasn't done so already? Where is AI lacking today? What could it do more of? Ideas can be grounded such as AI robots serving as caretakers for the elderly in their homes or more sci fi-like such as having AI colonize Mars for us instead of humans. Go wild! Would love to hear your thoughts! submitted by /u/Current-Development5 [link] [comments]  ( 1 min )

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According to the World Economic Forum's "The Future of Jobs Report 2020", AI is expected to replace 85 million jobs worldwide by 2025. So if AI gets smarter and replace our jobs, we don’t have to work anymore but we can get whatever we need for a minimum standard of living without paying money? submitted by /u/Dayoshiime18 [link] [comments]  ( 2 min )

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If you are passionate about AI, please consider joining our community and come exchange with us! We are a community around AI called "Learn AI Together". I think the name says it all, but more specifically, we are a community on Discord with ~22'000 members looking for more professionals to help others, but also for anyone interested in AI that is willing to connect with people like-minded. We share job offers, interesting projects, events. You can have discussions regarding pretty much anything related to AI with people that will be just as excited as you are! I think it's a fantastic place to be if you are in the field or simply interested in it! I will also be looking for more contributors and moderators to help us if you are interested in that role, if so, please DM me on discord after joining the server, I'd be happy to chat! If that sounds interesting, please join here: https://www.discord.gg/learnaitogether submitted by /u/OnlyProggingForFun [link] [comments]  ( 1 min )

Fine-grained visual classification, in contrast to generic object classification, tries to correctly classify things from the same basic category (birds, vehicles, etc.) into subcategories. Because of the modest inter-class variances and high intra-class variations, Fine-Grained Visual Classification (FGVC) has long been regarded as a difficult assignment. The most common FGVC techniques, such as the part-based model and the attention-based model, are primarily focused on how to get the network to focus on the most discriminative regions. The inductive bias of localization is introduced to neural networks with elaborate structure, inspired by human observation behavior. Furthermore, when some species are virtually indistinguishable, human specialists frequently rely on information other …  ( 1 min )

I'm very passionate in intelligence and AI, but not too passionate in coding. I'm asking if there's any jobs out there in the AI industry that would require knowledge of the human mind, rather than programming, as that would be the ultimate career for me to get into. Any suggestions are appreciated. submitted by /u/Sinful0ne [link] [comments]  ( 2 min )

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https://venturebeat.com/2021/11/15/astera-labs-announces-memory-acceleration-to-clear-datacenter-ai-ml-bottlenecks/ Do you think this technology would allow the use of clusters to handle larger data sets, thus reducing the overall cost of doing ML? submitted by /u/sawine [link] [comments]  ( 1 min )